siloqy/prod/nautilus_event_trader.py

#!/usr/bin/env python3
"""
DOLPHIN Nautilus Event-Driven Trader
"""
import sys
import json
import hashlib
import math
import os
import time
import signal
import threading
import urllib.request
import uuid
from typing import Optional
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime, timezone
from pathlib import Path
from collections import deque

# Stablecoins / pegged assets that must never be traded
_STABLECOIN_SYMBOLS = frozenset({
    'USDCUSDT', 'BUSDUSDT', 'FDUSDUSDT', 'USDTUSDT', 'TUSDUSDT',
    'DAIUSDT',  'FRAXUSDT', 'USDDUSDT',  'USTCUSDT', 'EURUSDT',
})

sys.path.insert(0, '/mnt/dolphinng5_predict')
sys.path.insert(0, '/mnt/dolphinng5_predict/nautilus_dolphin')

from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDPosition
from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
from nautilus_dolphin.nautilus.ob_features import OBFeatureEngine
from nautilus_dolphin.nautilus.ob_provider import MockOBProvider
from nautilus_dolphin.nautilus.esof_size_gate import (
    parse_esof_payload, esof_gate_from_payload, esof_score_from_payload,
    esof_size_mult_from_score, ESOF_STALE_FALLBACK_MULT, ESOF_FRESHNESS_S,
)
try:
    sys.path.insert(0, '/mnt/dolphinng5_predict/Observability')
    from esof_advisor import compute_esof as _compute_esof_inline
except Exception:
    _compute_esof_inline = None
try:
    from adaptive_exit.market_state_runtime import MarketStateRuntime
except Exception:
    MarketStateRuntime = None
try:
    from adaptive_exit.advanced_sl import AdvancedSLRuntime
except Exception:
    AdvancedSLRuntime = None
try:
    from adaptive_exit.sc_threshold_advisor import SCThresholdAdvisor
except Exception:
    SCThresholdAdvisor = None
try:
    from adaptive_exit.sc_gauge_advisor import SCGaugeAdvisor, build_obf_snapshot_from_engine
except Exception:
    SCGaugeAdvisor = None
    build_obf_snapshot_from_engine = None
try:
    from adaptive_exit.bounce_advisor import BounceAdvisor
except Exception:
    BounceAdvisor = None
try:
    from adaptive_exit.post_win_long_overlay import PostWinExecutionFSM
except Exception:
    PostWinExecutionFSM = None
try:
    from nautilus_dolphin.nautilus.alpha_exit_v7_engine import AlphaExitEngineV7, TradeContextV7
except Exception:
    AlphaExitEngineV7 = None
    TradeContextV7 = None

BLUE_CH_DB = "dolphin"

try:
    from prod.ch_writer import ch_put, ts_us as _ch_ts_us
except ImportError:
    def ch_put(*a, **kw): pass
    def _ch_ts_us(): return 0

try:
    from announcement_router import build_announcement_center
except ImportError:
    from prod.announcement_router import build_announcement_center

sys.path.insert(0, '/mnt/dolphinng5_predict/prod')
from dolphin_exit_handler import install_exit_handler
install_exit_handler("nautilus_trader")

HZ_CLUSTER = "dolphin"
HZ_HOST = "127.0.0.1:5701"
EIGEN_DIR = Path('/mnt/dolphinng6_data/eigenvalues')

CAPITAL_DISK_CHECKPOINT = Path("/tmp/dolphin_capital_checkpoint.json")
ANNOUNCEMENT_CONFIG = Path("/mnt/dolphinng5_predict/prod/configs/position_notifications_blue.json")
ANNOUNCEMENT_RUNTIME_ENV = Path("/mnt/dolphin_training/observability_notifications_blue.runtime.json")

ENGINE_KWARGS = dict(
    initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05,
    min_leverage=0.5, max_leverage=8.0,   # note: create_d_liq_engine overrides to D_LIQ_SOFT_CAP=8.0
    leverage_convexity=3.0,
    fraction=0.20, fixed_tp_pct=0.0020, stop_pct=1.0, max_hold_bars=250,  # TP research 2026-05-11: 0.95→0.20%
    use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
    dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
    use_asset_selection=True, min_irp_alignment=0.0,  # gold spec: no IRP filter
    use_sp_fees=True, use_sp_slippage=True,
    sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50,
    use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
    lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
    allow_subday_acb_exit=False,
)


def _env_bool(name: str, default: bool) -> bool:
    raw = os.environ.get(name)
    if raw is None:
        return default
    return str(raw).strip().lower() in {"1", "true", "yes", "on"}


def _direction_from_env(value: Optional[str] = None) -> int:
    raw = os.environ.get("DOLPHIN_DIRECTION", "short_only") if value is None else value
    text = str(raw or "short_only").strip().lower()
    if text in {"short", "short_only", "sell", "-1"}:
        return -1
    if text in {"long", "long_only", "buy", "+1", "1"}:
        return 1
    raise ValueError(
        f"Unsupported DOLPHIN_DIRECTION={raw!r}; use short_only or long_only"
    )


def _direction_label(direction: int) -> str:
    return "LONG" if int(direction) == 1 else "SHORT"


def _normalize_v7_exit_reason(reason: str) -> str:
    text = str(reason or "").strip()
    if text == "V7_MAE_SL_VOL_NORM":
        return "V7.1_MAE_SL_VOL_NORM"
    return text


def _safe_float(value, default: float = 0.0) -> float:
    try:
        out = float(value)
    except (TypeError, ValueError):
        return default
    return out if math.isfinite(out) else default


def _flatten_env_payload(payload, prefix: str = "") -> dict:
    flat = {}
    if not isinstance(payload, dict):
        return flat
    for key, value in payload.items():
        if not isinstance(key, str) or not key.strip():
            continue
        full_key = f"{prefix}_{key}" if prefix else key
        if isinstance(value, dict):
            flat.update(_flatten_env_payload(value, full_key))
        else:
            flat[full_key.upper()] = value
    return flat


def _seed_runtime_env(path: Path) -> None:
    if not path.exists():
        return
    try:
        payload = json.loads(path.read_text())
    except Exception:
        return
    for key, value in _flatten_env_payload(payload).items():
        if key not in os.environ and value not in (None, "", "__CHANGE_ME__", "__REPLACE_ME__"):
            os.environ[key] = str(value)

BTC_VOL_WINDOW = 50

# Per-bucket SL % used when HIBERNATE fires while a position is open.
# Instead of immediate HIBERNATE_HALT, we arm TP (existing fixed_tp_pct) +
# a per-bucket stop-loss so the position exits cleanly rather than being
# force-closed at whatever price the halt fires at.
# Values derived from AE shadow data + bucket trade analysis (2026-04-19).
#   B3 wide: shadow shows mae_norm 5-5.1 before FIXED_TP; 3.5×ATR fires on noise.
#   B4 tight: 34.8% WR, 0.80 R:R — cut fast, no recovery value.
#   B6 widest: extreme vol (vol_daily_pct 760-864); normal ATR excursions are large.
_BUCKET_SL_PCT: dict = {
    0: 0.015,   # Low-vol high-corr nano-cap
    1: 0.012,   # Med-vol low-corr mid-price (XRP/XLM class)
    2: 0.015,   # Mega-cap BTC/ETH — default (not traded)
    3: 0.025,   # High-vol mid-corr STAR bucket (ENJ/ADA/DOGE) — needs room
    4: 0.008,   # Worst bucket (BNB/LTC/LINK) — cut fast
    5: 0.018,   # High-vol low-corr micro-price (ATOM/TRX class)
    6: 0.030,   # Extreme-vol mid-corr (FET/ZRX) — widest
    'default': 0.015,
}
# Gold-calibrated from full 5-year BTC history: 0.00026414 (stricter, ~2.7x tighter).
# 2026-04-07: switched to 56-day gold window value (0.00009868) — the exact threshold
# used in the T=2155 ROI=+189% backtest. More permissive; paper trading to gather data.
# 2026-05-09 weekend mode: runtime-configurable lower gate for low-vol tape.
#
# Legacy references preserved:
#   VOL_P60_THRESHOLD_LEGACY_MAIN = 0.00026414
#   VOL_P60_THRESHOLD_GOLD_56D    = 0.00009868
VOL_P60_THRESHOLD_LEGACY_MAIN = 0.00026414
VOL_P60_THRESHOLD_GOLD_56D = 0.00009868
VOL_P60_THRESHOLD_WEEKEND_DEFAULT = 0.00003
VOL_P60_THRESHOLD_RELAXED_TEMP = 0.00015838


def _vol_p60_threshold_from_env(default: float = VOL_P60_THRESHOLD_LEGACY_MAIN) -> float:
    raw = os.environ.get("DOLPHIN_VOL_P60_THRESHOLD")
    if raw is None:
        return float(default)
    try:
        out = float(str(raw).strip())
    except Exception:
        return float(default)
    if not math.isfinite(out) or out <= 0.0:
        return float(default)
    return float(out)

# Algorithm Versioning
# v1_shakedown: v50-v150 (noise bug), loose vol gate
# v2_gold_fix: CORRECTED v50-v750 macro divergence (matches parquet backtest)
ALGO_VERSION = "v2_gold_fix_v50-v750"

# Persistent, version-tagged trade log (survives reboots; sorts by date)
_LOG_DIR = "/mnt/dolphinng5_predict/prod/logs"
os.makedirs(_LOG_DIR, exist_ok=True)
_LOG_DATE = datetime.now(timezone.utc).strftime("%Y%m%d")
TRADE_LOG = f"{_LOG_DIR}/nautilus_trader_{_LOG_DATE}_{ALGO_VERSION}.log"
running = True

def log(msg):
    ts = datetime.now(timezone.utc).isoformat()
    line = f"[{ts}] {msg}"
    print(line, flush=True)
    with open(TRADE_LOG, 'a') as f:
        f.write(line + '\n')


def _chain_digest(payload: dict) -> str:
    """Stable digest for BLUE exit-chain state."""
    body = json.dumps(payload, sort_keys=True, separators=(",", ":"), default=str).encode()
    return hashlib.sha256(body).hexdigest()


def _build_chain_state(
    *,
    trade_id: str,
    asset: str,
    side: str,
    entry_price: float,
    quantity: float,
    notional: float,
    entry_bar: int,
    entry_ts: int,
    retraction_legs: int = 0,
    realized_pnl_legs_total: float = 0.0,
    chain_root_trade_id: str | None = None,
    chain_head_leg_id: str | None = None,
    chain_prev_leg_id: str = "",
    chain_mode: str = "LIVE",
) -> dict:
    """Build a deterministic chain snapshot for the current open trade head."""
    root = str(chain_root_trade_id or trade_id or "")
    seq = max(0, int(retraction_legs))
    head = str(chain_head_leg_id or (f"{trade_id}:open" if seq <= 0 else f"{trade_id}:x{seq:03d}"))
    prev = str(chain_prev_leg_id or "")
    anchor = {
        "trade_id": str(trade_id or ""),
        "chain_root_trade_id": root,
        "chain_head_leg_id": head,
        "chain_prev_leg_id": prev,
        "chain_seq": seq,
        "chain_mode": str(chain_mode or "LIVE"),
        "asset": str(asset or ""),
        "side": str(side or "").upper(),
        "entry_price": round(float(entry_price or 0.0), 12),
        "quantity": round(float(quantity or 0.0), 12),
        "notional": round(float(notional or 0.0), 12),
        "entry_bar": int(entry_bar or 0),
        "entry_ts": int(entry_ts or 0),
        "retraction_legs": seq,
        "realized_pnl_legs_total": round(float(realized_pnl_legs_total or 0.0), 12),
    }
    anchor["chain_token"] = _chain_digest(anchor)
    anchor["chain_version"] = 1
    anchor["chain_kind"] = "ROOT" if seq <= 0 else "LEG"
    return anchor

class DolphinLiveTrader:
    def __init__(self):
        self.eng = None
        self.hz_client = None
        self.features_map = None
        self.safety_map = None
        self.pnl_map = None
        self.state_map = None
        self.heartbeat_map = None
        self.control_map = None
        self.eng_lock = threading.Lock()
        self._heartbeat_stop = threading.Event()
        self._dedup_lock = threading.Lock()   # guards atomic check-and-set on last_scan_number
        self._scan_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="scan")
        self.last_scan_number = -1
        self.last_file_mtime = 0
        self.bar_idx = 0
        self.current_day = None
        self.trades_executed = 0
        self.scans_processed = 0
        self.btc_prices = deque(maxlen=BTC_VOL_WINDOW + 2)
        self.cached_posture = "APEX"
        self.posture_cache_time = 0
        self.ob_assets = []
        self.ob_eng = None
        self.acb = None
        self.last_w750_vel = None
        self._pending_entries: dict = {}   # trade_id → entry snapshot (for CH trade_events)
        self._last_exf: dict = {}
        self._exf_log_time = 0.0          # throttle for on_exf_update logging
        self._ae = None                    # AdaptiveExitEngine shadow (parallel, never real exits)
        self._v7_exit_engine = None        # AlphaExitEngineV7 live BLUE exit control + journal
        self._v7_contexts: dict = {}       # trade_id → TradeContextV7
        self._v7_decisions: dict = {}      # trade_id → latest v7 decision
        self._v7_decision_seq: dict = {}   # trade_id → monotonic eval sequence
        self._v7_journal_enabled: bool = _env_bool("DOLPHIN_ENABLE_V7_JOURNAL", True)
        self._v7_journal_db: str = BLUE_CH_DB
        self._v7_journal_table: str = "v7_decision_events"
        self._v7_live_exit_enabled: bool = False
        self._sc_advisor = None            # SC threshold advisor (shadow-only)
        self._sc_advisor_last_log = 0.0
        self._sc_gauge = None              # SC bucket gauge advisor (shadow-only)
        self._sc_gauge_last_log = 0.0
        self._bounce_advisor = None        # inverse-ARS bounce advisor (shadow-only)
        self._bounce_advisor_last_log = 0.0
        self._bounce_price_history: dict[str, deque] = {}
        self._market_state_runtime = MarketStateRuntime() if MarketStateRuntime is not None else None
        self._advanced_sl = AdvancedSLRuntime.load() if AdvancedSLRuntime is not None else None
        self._hibernate_protect_active: str | None = None  # trade_id being protected
        self._bucket_assignments: dict = {}  # asset → KMeans bucket_id (loaded from pkl)
        self._last_esof_size_mult: float = 1.0
        self._restore_failed: bool = False
        self._restore_failure_reason: str = ""
        self._restore_source: str = ""
        self.trade_direction: int = _direction_from_env()
        self.vol_p60_threshold: float = _vol_p60_threshold_from_env()
        self._runtime_direction: int = self.trade_direction
        self._efsm = PostWinExecutionFSM() if PostWinExecutionFSM is not None else None
        self._trade_announcement_center = None
        self._processed_retract_commands: deque = deque(maxlen=5000)
        self._processed_retract_set: set[str] = set()
        _seed_runtime_env(ANNOUNCEMENT_RUNTIME_ENV)
        if ANNOUNCEMENT_CONFIG.exists():
            try:
                self._trade_announcement_center = build_announcement_center(
                    ANNOUNCEMENT_CONFIG,
                    hz_getter=self._get_hz,
                    logger=None,
                )
                log("  Position announcements: loaded")
            except Exception as e:
                log(f"  Position announcements: {e}")
                self._trade_announcement_center = None
        if self._efsm is not None:
            log("  EFSM: loaded (post-win LONG overlay)")
        if self._advanced_sl is not None:
            log("  AdvancedSL: loaded (shadow prototype)")

    def _resolve_runtime_direction(self) -> int:
        """Resolve active trade direction for the next eligible entry."""
        base = int(self.trade_direction)
        if base != -1 or self._efsm is None:
            return base
        with self.eng_lock:
            has_open_position = getattr(self.eng, "position", None) is not None
        if has_open_position:
            return base
        return 1 if int(self._efsm.pending_slots) > 0 else base

    def _apply_runtime_direction(self) -> None:
        """Apply current runtime direction to the engine regime."""
        resolved = self._resolve_runtime_direction()
        with self.eng_lock:
            if getattr(self.eng, "regime_direction", self.trade_direction) != resolved:
                self.eng.regime_direction = resolved
        self._runtime_direction = resolved

    def _build_engine(self):
        log("Building NDAlphaEngine...")
        engine_kwargs = dict(ENGINE_KWARGS)
        engine_kwargs["allow_subday_acb_exit"] = _env_bool(
            "DOLPHIN_ALLOW_ACB_SUBDAY_EXIT",
            bool(engine_kwargs.get("allow_subday_acb_exit", False)),
        )
        self.eng = create_d_liq_engine(**engine_kwargs)
        log(f"  Engine: {type(self.eng).__name__}")
        log(f"  Direction: {_direction_label(self.trade_direction)} ({self.trade_direction:+d})")
        log(
            "  VOL gate threshold: "
            f"{self.vol_p60_threshold:.8f} "
            f"(legacy_main={VOL_P60_THRESHOLD_LEGACY_MAIN:.8f}, gold_56d={VOL_P60_THRESHOLD_GOLD_56D:.8f}, "
            f"relaxed_temp={VOL_P60_THRESHOLD_RELAXED_TEMP:.7f})"
        )
        log(f"  ACB subday exits: {'ON' if engine_kwargs['allow_subday_acb_exit'] else 'OFF'}")
        log(f"  Leverage: soft={self.eng.base_max_leverage}x abs={self.eng.abs_max_leverage}x")
        
        if EIGEN_DIR.exists():
            try:
                date_strings = sorted([d.name for d in EIGEN_DIR.iterdir() if d.is_dir()])
                self.acb = AdaptiveCircuitBreaker()
                self.acb.preload_w750(date_strings)
                self.eng.set_acb(self.acb)
                log("  ACBv6: loaded")
            except Exception as e:
                log(f"  ACBv6: {e}")
        else:
            self.acb = AdaptiveCircuitBreaker()
            self.eng.set_acb(self.acb)
            log("  ACBv6: loaded (no preload dates)")
        
        self.eng.set_esoteric_hazard_multiplier(0.0)  # gold spec: init guard, MUST precede set_mc_forewarner
        log(f"  Hazard: set_esoteric_hazard_multiplier(0.0) — soft={self.eng.base_max_leverage}x")

        MC_MODELS_DIR = '/mnt/dolphinng5_predict/nautilus_dolphin/mc_results/models'
        MC_BASE_CFG = {
            'trial_id': 0, 'vel_div_threshold': -0.020, 'vel_div_extreme': -0.050,
            'use_direction_confirm': True, 'dc_lookback_bars': 7,
            'dc_min_magnitude_bps': 0.75, 'dc_skip_contradicts': True,
            'dc_leverage_boost': 1.00, 'dc_leverage_reduce': 0.50,
            'vd_trend_lookback': 10, 'min_leverage': 0.50, 'max_leverage': 8.00,  # gold spec
            'leverage_convexity': 3.00, 'fraction': 0.20, 'use_alpha_layers': True,
            'use_dynamic_leverage': True, 'fixed_tp_pct': 0.0020, 'stop_pct': 1.00,
            'max_hold_bars': 250, 'use_sp_fees': True, 'use_sp_slippage': True,  # gold spec
            'sp_maker_entry_rate': 0.62, 'sp_maker_exit_rate': 0.50,
            'use_ob_edge': True, 'ob_edge_bps': 5.00, 'ob_confirm_rate': 0.40,
            'ob_imbalance_bias': -0.09, 'ob_depth_scale': 1.00,
            'use_asset_selection': True, 'min_irp_alignment': 0.0,
            'asset_selector_lookback': 10, 'lookback': 100,  # gold spec
            'acb_beta_high': 0.80, 'acb_beta_low': 0.20, 'acb_w750_threshold_pct': 60,
        }
        if Path(MC_MODELS_DIR).exists():
            try:
                from mc.mc_ml import DolphinForewarner
                forewarner = DolphinForewarner(models_dir=MC_MODELS_DIR)
                self.eng.set_mc_forewarner(forewarner, MC_BASE_CFG)
                log("  MC-Forewarner: wired")
            except Exception as e:
                log(f"  MC-Forewarner: {e}")

        try:
            from adaptive_exit.adaptive_exit_engine import AdaptiveExitEngine
            self._ae = AdaptiveExitEngine.load()
            log("  AdaptiveExitEngine: loaded (shadow mode — no real exits)")
        except Exception as e:
            log(f"  AdaptiveExitEngine: {e} — shadow disabled")

        if AlphaExitEngineV7 is not None and self._v7_journal_enabled:
            try:
                self._v7_exit_engine = AlphaExitEngineV7(bar_duration_sec=11.0)
                self._ensure_v7_journal_table()
                log("  AlphaExitEngineV7: loaded (live BLUE exit control + journal)")
            except Exception as e:
                log(f"  AlphaExitEngineV7: {e} — shadow disabled")
                self._v7_exit_engine = None
        self._v7_live_exit_enabled = self._v7_exit_engine is not None
        if self.eng is not None:
            self.eng.exit_decision_provider = self._v7_live_exit_decision if self._v7_live_exit_enabled else None

        self._load_bucket_assignments()

        if SCThresholdAdvisor is not None:
            try:
                self._sc_advisor = SCThresholdAdvisor.load(
                    strategy="blue",
                    shadow_db=BLUE_CH_DB,
                )
                log("  SCThresholdAdvisor: loaded (shadow mode — no sizing changes)")
            except Exception as e:
                log(f"  SCThresholdAdvisor: {e} — shadow disabled")
                self._sc_advisor = None

        if SCGaugeAdvisor is not None:
            try:
                self._sc_gauge = SCGaugeAdvisor.load(
                    strategy="blue",
                    shadow_db=BLUE_CH_DB,
                )
                log("  SCGaugeAdvisor: loaded (shadow mode — no sizing changes)")
            except Exception as e:
                log(f"  SCGaugeAdvisor: {e} — shadow disabled")
                self._sc_gauge = None

        if BounceAdvisor is not None:
            try:
                self._bounce_advisor = BounceAdvisor.load(
                    strategy="blue",
                    shadow_db=BLUE_CH_DB,
                )
                log("  BounceAdvisor: loaded (shadow mode — no execution changes)")
            except Exception as e:
                log(f"  BounceAdvisor: {e} — shadow disabled")
                self._bounce_advisor = None

    def _load_bucket_assignments(self):
        """Load KMeans asset→bucket_id mapping for hibernate protection SL levels."""
        try:
            import pickle
            pkl_path = Path('/mnt/dolphinng5_predict/adaptive_exit/models/bucket_assignments.pkl')
            with open(pkl_path, 'rb') as f:
                data = pickle.load(f)
            self._bucket_assignments = data.get('assignments', {})
            log(f"  BucketAssignments: {len(self._bucket_assignments)} assets loaded for hibernate protection")
        except Exception as e:
            log(f"  BucketAssignments: {e} — hibernate protect will use default SL={_BUCKET_SL_PCT['default']*100:.1f}%")

    def _announce_position_event(
        self,
        *,
        kind: str,
        severity: str,
        title: str,
        message: str,
        metadata: dict | None = None,
    ) -> None:
        center = getattr(self, "_trade_announcement_center", None)
        if center is None:
            return
        try:
            center.note_event(
                kind=kind,
                severity=severity,
                title=title,
                message=message,
                metadata=metadata or {},
            )
        except Exception as e:
            log(f"  Position announcement failed: {e}")

    def _read_esof_payload(self) -> dict | None:
        """Read the freshest EsoF advisory payload from HZ, if available."""
        if not self.features_map:
            return None
        for key in ("esof_latest", "esof_advisor_latest"):
            try:
                raw = self.features_map.blocking().get(key)
            except Exception:
                continue
            payload = parse_esof_payload(raw)
            if payload:
                return payload
        return None

    def _sync_esof_size_gate(self) -> None:
        """Update the shared engine with the current continuous EsoF size multiplier.

        When the HZ payload is stale or missing (daemon died, HZ restarted),
        falls back to inline computation using the canonical compute_esof() from
        esof_advisor.py — single implementation, no parallel code.
        """
        payload = self._read_esof_payload()
        score = esof_score_from_payload(payload, max_age_s=ESOF_FRESHNESS_S)
        source = "hz"

        if score is None and _compute_esof_inline is not None:
            try:
                inline = _compute_esof_inline()
                score = esof_score_from_payload(inline, max_age_s=None)
                if score is not None:
                    source = "inline"
                    payload = inline
            except Exception:
                pass

        mult = esof_size_mult_from_score(score)
        with self.eng_lock:
            if hasattr(self.eng, "set_esof_advisory_score"):
                self.eng.set_esof_advisory_score(score)
        if mult != self._last_esof_size_mult:
            self._last_esof_size_mult = mult
            if score is None:
                log(f"EsoF size gate: STALE-FALLBACK mult={mult:.2f} (no HZ + no inline)")
            elif source == "inline":
                log(f"EsoF size gate: INLINE sc={score:+.3f} mult={mult:.2f} (HZ stale)")
            else:
                log(f"EsoF size gate: sc={score:+.3f} mult={mult:.2f}")

    def _sync_tp_threshold(self) -> None:
        """Read live TP threshold from HZ control plane and propagate to engine.

        HZ key: DOLPHIN_FEATURES["live_tp_threshold"] → JSON {"tp_pct": 0.0020, "ts": ...}
        If absent or stale, keeps the current default (0.0020 from ENGINE_KWARGS).
        A tighter TP cuts open positions immediately; a wider TP extends the hold.
        """
        if not self.features_map:
            return
        try:
            raw = self.features_map.blocking().get("live_tp_threshold")
            if not raw:
                return
            payload = json.loads(raw) if isinstance(raw, str) else raw
            tp_pct = float(payload.get("tp_pct", 0))
            if tp_pct <= 0:
                return
            with self.eng_lock:
                old = self.eng.set_live_tp_pct(tp_pct)
            if abs(old - tp_pct) > 1e-6:
                log(f"TP threshold: {old*100:.2f}% → {tp_pct*100:.2f}% (HZ control plane)")
        except Exception:
            pass

    def _inject_obf_midprice(self, prices_dict: dict) -> dict:
        """Override scan price for the open position's asset with live OB mid-price.

        Scan prices are quantized to ~4 decimal places (e.g. 0.1255 vs 0.1256),
        which is too coarse for a 0.20% TP on low-priced assets. The OBF universe
        service has live WebSocket bid/ask at ~0.1s resolution with full precision.
        This method substitutes the scan price with (best_bid + best_ask) / 2 for
        the position's asset only, so TP evaluation sees the true market price.
        """
        pos = self.eng.position
        if pos is None or not pos.asset:
            return prices_dict
        try:
            raw = self.features_map.blocking().get("obf_universe_latest")
            if not raw:
                return prices_dict
            obf = json.loads(raw)
            asset_data = obf.get(pos.asset)
            if not asset_data or not isinstance(asset_data, dict):
                return prices_dict
            best_bid = float(asset_data.get("best_bid", 0) or 0)
            best_ask = float(asset_data.get("best_ask", 0) or 0)
            if best_bid <= 0 or best_ask <= 0:
                return prices_dict
            mid = (best_bid + best_ask) / 2.0
            if pos.asset in prices_dict:
                scan_px = prices_dict[pos.asset]
                drift = abs(mid - scan_px) / scan_px if scan_px > 0 else 1.0
                if drift > 0.05:
                    return prices_dict
            out = dict(prices_dict)
            out[pos.asset] = mid
            return out
        except Exception:
            return prices_dict

    def _sync_sc_threshold_advisor(self, scan_number: int, vel_div: float) -> None:
        """Shadow-only advisory layer for tracking / future threshold learning."""
        if self._sc_advisor is None:
            return
        try:
            payload = self._read_esof_payload()
            trade_history = getattr(self.eng, "trade_history", [])
            open_tid = next(iter(self._pending_entries.keys()), "")
            pending = self._pending_entries.get(open_tid, {}) if open_tid else {}
            rec = self._sc_advisor.evaluate(
                trade_id=str(open_tid or ""),
                asset=str(pending.get("asset", "")),
                sc=_safe_float(payload.get("advisory_score", payload.get("score", 0.0)) if payload else None),
                vel_div=float(vel_div or 0.0),
                exf_snapshot=getattr(self, "_last_exf", {}) or {},
                trade_history=trade_history,
                current_mult=float(self._last_esof_size_mult or 1.0),
                esof_payload=payload,
                scan_number=int(scan_number or 0),
                bar_idx=int(self.bar_idx),
                strategy="blue",
                log_shadow=True,
            )
            if open_tid:
                pending["sc_threshold_advisor"] = rec
                pending["sc_exec_mult"] = float(self._last_esof_size_mult or 1.0)
                self._pending_entries[open_tid] = pending
            now = time.time()
            if now - self._sc_advisor_last_log >= 300:
                self._sc_advisor_last_log = now
                log(
                    f"SC_ADVISOR: sc={rec['sc']:+.3f} cur={rec['current_mult']:.2f} "
                    f"rec={rec['recommended_mult']:.2f} cut={rec['recommended_sc_cut']:+.2f} "
                    f"conf={rec['confidence']:.2f} src={rec['decision_source']}"
                )
        except Exception as e:
            log(f"SC_ADVISOR error: {e}")

    def _current_obf_snapshot(self, asset: str, bar_idx: int) -> dict[str, dict]:
        if build_obf_snapshot_from_engine is None or self.ob_eng is None or not asset:
            return {}
        try:
            return build_obf_snapshot_from_engine(self.ob_eng, asset, bar_idx)
        except Exception:
            return {}

    def _record_bounce_prices(self, prices_dict: dict[str, float]) -> None:
        """Maintain rolling price histories for the bounce advisor."""
        if not prices_dict:
            return
        for asset, px in prices_dict.items():
            try:
                price = float(px)
            except Exception:
                continue
            if not math.isfinite(price) or price <= 0.0:
                continue
            hist = self._bounce_price_history.get(asset)
            if hist is None:
                hist = deque(maxlen=512)
                self._bounce_price_history[asset] = hist
            hist.append(price)

    def _bounce_price_path(self, asset: str) -> list[float]:
        hist = self._bounce_price_history.get(asset)
        if not hist:
            return []
        return [float(px) for px in hist if math.isfinite(float(px))]

    def _bounce_eval(
        self,
        *,
        trade_id: str,
        asset: str,
        side: str,
        source: str,
        scan_number: int,
        entry_ts: datetime | None,
        current_price: float,
        entry_price: float,
        quantity: float,
        notional: float,
        leverage: float,
        vel_div: float,
        current_mult: float,
        bars_held: int,
        log_shadow: bool = True,
    ) -> dict | None:
        """Evaluate the bounce advisor on a rolling price path and persist the row."""
        if self._bounce_advisor is None or not trade_id or not asset:
            return None
        price_path = self._bounce_price_path(asset)
        if len(price_path) < 3:
            return None
        rec = self._bounce_advisor.evaluate(
            trade_id=str(trade_id),
            asset=str(asset),
            side=str(side or "SHORT"),
            price_path=price_path,
            entry_ts=entry_ts or datetime.now(timezone.utc),
            entry_price=float(entry_price or 0.0),
            current_price=float(current_price or 0.0),
            quantity=float(quantity or 0.0),
            notional=float(notional or 0.0),
            leverage=float(leverage or 0.0),
            current_mult=float(current_mult or 1.0),
            vel_div=float(vel_div or 0.0),
            scan_number=int(scan_number or 0),
            bar_idx=int(self.bar_idx),
            bars_held=int(max(0, bars_held)),
            source=str(source or "entry"),
            obf_snapshot=self._current_obf_snapshot(asset, self.bar_idx),
            log_shadow=log_shadow,
            use_ta=True,
            use_obf=True,
        )
        if rec:
            rec["price_path"] = price_path[-128:]
        return rec

    def _ensure_v7_journal_table(self) -> None:
        """Create the V7 decision journal if it does not already exist."""
        ddl = f"""
        CREATE TABLE IF NOT EXISTS {self._v7_journal_db}.{self._v7_journal_table}
        (
            ts DateTime64(6, 'UTC'),
            ts_day Date MATERIALIZED toDate(ts),
            strategy LowCardinality(String),
            source LowCardinality(String),
            trade_id String,
            asset LowCardinality(String),
            side LowCardinality(String),
            entry_price Float64,
            current_price Float64,
            quantity Float64,
            notional Float64,
            leverage Float32,
            bar_idx UInt32,
            decision_seq UInt32,
            bars_held UInt16,
            action LowCardinality(String),
            reason LowCardinality(String),
            pnl_pct Float32,
            mfe Float32,
            mae Float32,
            mfe_risk Float32,
            mae_risk Float32,
            exit_pressure Float32,
            rv_comp Float32,
            mae_thresh1 Float32,
            bounce_score Float32,
            bounce_risk Float32,
            ob_imbalance Float32,
            vel_div_entry Float32,
            vel_div_now Float32,
            v50_vel Float32,
            v750_vel Float32,
            exf_funding Float32,
            exf_dvol Float32,
            exf_fear_greed Float32,
            exf_taker Float32,
            posture LowCardinality(String)
        )
        ENGINE = MergeTree
        PARTITION BY toYYYYMM(ts)
        ORDER BY (ts_day, trade_id, decision_seq, ts)
        TTL ts_day + toIntervalDay(180)
        """
        try:
            req = urllib.request.Request(
                "http://localhost:8123/",
                data=ddl.encode(),
                method="POST",
            )
            req.add_header("X-ClickHouse-User", "dolphin")
            req.add_header("X-ClickHouse-Key", "dolphin_ch_2026")
            urllib.request.urlopen(req, timeout=5).close()
        except Exception as exc:
            log(f"[V7_JOURNAL] table ensure failed: {exc}")

    def _record_v7_decision(
        self,
        *,
        trade_id: str,
        asset: str,
        side: str,
        decision: dict,
        current_price: float,
        ob_imbalance: float,
        vel_div_now: float,
        v50_vel: float,
        v750_vel: float,
        source: str = "scan_eval",
        bar_idx: int | None = None,
    ) -> None:
        """Persist a V7 evaluation for observability and offline comparison."""
        if not self._v7_journal_enabled or self._v7_exit_engine is None:
            return
        pending = self._pending_entries.get(trade_id, {})
        seq = int(self._v7_decision_seq.get(trade_id, 0)) + 1
        self._v7_decision_seq[trade_id] = seq
        entry_price = float(pending.get("entry_price", 0.0) or 0.0)
        quantity = float(pending.get("quantity", 0.0) or 0.0)
        row = {
            "ts":            _ch_ts_us(),
            "strategy":      "blue",
            "source":        source,
            "trade_id":      str(trade_id or ""),
            "asset":         str(asset or pending.get("asset", "")),
            "side":          str(side or pending.get("side", "")),
            "entry_price":   entry_price,
            "current_price": float(current_price or 0.0),
            "quantity":      quantity,
            "notional":      float(quantity * entry_price),
            "leverage":      float(pending.get("leverage", 0.0) or 0.0),
            "bar_idx":       int(max(0, self.bar_idx - 1 if bar_idx is None else bar_idx)),
            "decision_seq":  seq,
            "bars_held":     int(decision.get("bars_held", 0) or 0),
            "action":        str(decision.get("action", "UNKNOWN") or "UNKNOWN"),
            "reason":        _normalize_v7_exit_reason(decision.get("reason") or ""),
            "pnl_pct":       float(decision.get("pnl_pct", 0.0) or 0.0),
            "mfe":           float(decision.get("mfe", 0.0) or 0.0),
            "mae":           float(decision.get("mae", 0.0) or 0.0),
            "mfe_risk":      float(decision.get("mfe_risk", 0.0) or 0.0),
            "mae_risk":      float(decision.get("mae_risk", 0.0) or 0.0),
            "exit_pressure": float(decision.get("exit_pressure", 0.0) or 0.0),
            "rv_comp":       float(decision.get("rv_comp", 0.0) or 0.0),
            "mae_thresh1":   float(decision.get("mae_thresh1", 0.0) or 0.0),
            "bounce_score":  float(decision.get("bounce_score", 0.0) or 0.0),
            "bounce_risk":   float(decision.get("bounce_risk", 0.0) or 0.0),
            "ob_imbalance":  float(ob_imbalance or 0.0),
            "vel_div_entry": float(pending.get("vel_div_entry", 0.0) or 0.0),
            "vel_div_now":   float(vel_div_now or 0.0),
            "v50_vel":       float(v50_vel or 0.0),
            "v750_vel":      float(v750_vel or 0.0),
            "exf_funding":   float(self._last_exf.get("funding", 0.0) or 0.0),
            "exf_dvol":      float(self._last_exf.get("dvol", 0.0) or 0.0),
            "exf_fear_greed": float(self._last_exf.get("fear_greed", 0.0) or 0.0),
            "exf_taker":     float(self._last_exf.get("taker", 0.0) or 0.0),
            "posture":       str(pending.get("posture", self.cached_posture) or ""),
        }
        try:
            ch_put(self._v7_journal_table, row)
        except Exception as exc:
            log(f"[V7_JOURNAL] write failed: {exc}")

    def _v7_live_exit_decision(
        self,
        *,
        pos,
        bar_idx: int,
        prices: dict,
        vel_div: float,
        v50_vel: float,
        v750_vel: float,
    ) -> dict | None:
        """Live BLUE exit hook backed by AlphaExitEngineV7.

        The orchestrator calls this before falling back to the base exit manager.
        Returns a V7 decision dict or None if the trade cannot yet be evaluated.
        """
        if self._v7_exit_engine is None or pos is None:
            return None

        trade_id = str(getattr(pos, "trade_id", "") or "")
        asset = str(getattr(pos, "asset", "") or "")
        if not trade_id or not asset:
            return None

        pending = self._pending_entries.get(trade_id, {})
        ctx_v7 = self._v7_contexts.get(trade_id)
        eval_bar = max(0, int(bar_idx) - 1)

        if ctx_v7 is None:
            try:
                ctx_v7 = self._v7_exit_engine.make_context(
                    entry_price=float(
                        pending.get("entry_price", getattr(pos, "entry_price", 0.0))
                        or getattr(pos, "entry_price", 0.0)
                        or 0.0
                    ),
                    entry_bar=int(pending.get("entry_bar", eval_bar) or eval_bar),
                    side=1 if str(pending.get("side", "SHORT") or "SHORT") == "SHORT" else 0,
                )
                if self._last_exf:
                    ctx_v7.set_exf(
                        funding=float(self._last_exf.get("funding", 0.0) or 0.0),
                        dvol=float(self._last_exf.get("dvol", 0.0) or 0.0),
                        fear_greed=float(self._last_exf.get("fear_greed", 0.0) or 0.0),
                        taker=float(self._last_exf.get("taker", 0.0) or 0.0),
                    )
                self._v7_contexts[trade_id] = ctx_v7
                self._v7_decision_seq.setdefault(trade_id, 0)
            except Exception as exc:
                log(f"  V7 live context init failed for {trade_id}: {exc}")
                return None
        elif self._last_exf:
            try:
                ctx_v7.set_exf(
                    funding=float(self._last_exf.get("funding", 0.0) or 0.0),
                    dvol=float(self._last_exf.get("dvol", 0.0) or 0.0),
                    fear_greed=float(self._last_exf.get("fear_greed", 0.0) or 0.0),
                    taker=float(self._last_exf.get("taker", 0.0) or 0.0),
                )
            except Exception:
                pass

        ob_imb = 0.0
        if self.ob_eng is not None:
            try:
                ob_sig = self.ob_eng.get_signal(asset, float(eval_bar))
                ob_imb = float(getattr(ob_sig, "imbalance_ma5", 0.0) or 0.0)
            except Exception as exc:
                log(f"  V7 live OB signal failed for {trade_id}: {exc}")

        cur_px = float(
            prices.get(asset, getattr(pos, "current_price", 0.0))
            or getattr(pos, "current_price", 0.0)
            or 0.0
        )
        if cur_px <= 0.0:
            return None

        decision = self._v7_exit_engine.evaluate(
            ctx_v7,
            cur_px,
            eval_bar,
            ob_imb,
            asset=asset,
        )
        self._v7_decisions[trade_id] = decision
        self._record_v7_decision(
            trade_id=trade_id,
            asset=asset,
            side=str(pending.get("side", "SHORT") or "SHORT"),
            decision=decision,
            current_price=cur_px,
            ob_imbalance=ob_imb,
            vel_div_now=vel_div,
            v50_vel=v50_vel,
            v750_vel=v750_vel,
            source="live_exit",
            bar_idx=eval_bar,
        )

        action = str(decision.get("action", "HOLD") or "HOLD")
        if action != "HOLD":
            log(
                "  V7 live decision: "
                f"{trade_id} {asset} action={action} reason={decision.get('reason', '')} "
                f"pressure={float(decision.get('exit_pressure', 0.0) or 0.0):+.3f} "
                f"pnl_pct={float(decision.get('pnl_pct', 0.0) or 0.0):+.3f}"
            )
        return decision

    def _sync_sc_gauge_advisor(self, scan_number: int, vel_div: float) -> None:
        """Shadow-only bucket gauge advisory surface."""
        if self._sc_gauge is None:
            return
        try:
            payload = self._read_esof_payload()
            trade_history = getattr(self.eng, "trade_history", [])
            open_tid = next(iter(self._pending_entries.keys()), "")
            pending = self._pending_entries.get(open_tid, {}) if open_tid else {}
            asset = str(pending.get("asset", ""))
            rec = self._sc_gauge.evaluate(
                trade_id=str(open_tid or ""),
                asset=asset,
                sc=_safe_float(payload.get("advisory_score", payload.get("score", 0.0)) if payload else None),
                vel_div=float(vel_div or 0.0),
                exf_snapshot=getattr(self, "_last_exf", {}) or {},
                obf_snapshot=self._current_obf_snapshot(asset, self.bar_idx),
                trade_history=trade_history,
                current_mult=float(self._last_esof_size_mult or 1.0),
                esof_payload=payload,
                scan_number=int(scan_number or 0),
                bar_idx=int(self.bar_idx),
                strategy="blue",
                log_shadow=True,
            )
            if open_tid:
                pending["sc_bucket_gauge"] = rec
                pending["sc_bucket_gauge_exec_mult"] = float(self._last_esof_size_mult or 1.0)
                self._pending_entries[open_tid] = pending
            now = time.time()
            if now - self._sc_gauge_last_log >= 300:
                self._sc_gauge_last_log = now
                log(
                    f"SC_GAUGE: sc={rec['sc']:+.3f} bucket={rec['bucket_id']} "
                    f"cur={rec['current_mult']:.2f} rec={rec['recommended_size_mult']:.2f} "
                    f"tp={rec['recommended_tp_mult']:.2f} hold={rec['recommended_hold_mult']:.2f} "
                    f"cut={rec['recommended_sc_cut']:+.2f} conf={rec['confidence']:.2f}"
                )
        except Exception as e:
            log(f"SC_GAUGE error: {e}")

    def _resolve_trade_id(self, explicit: str | None = None, *, create_if_missing: bool = False) -> str:
        """Resolve a trade_id from the event, live position, or pending entry."""
        tid = str(explicit or "").strip()
        if tid:
            return tid
        pos = getattr(self.eng, "position", None)
        if pos is not None:
            pos_tid = str(getattr(pos, "trade_id", "") or "").strip()
            if pos_tid:
                return pos_tid
        if len(self._pending_entries) == 1:
            pending_tid = next(iter(self._pending_entries.keys()))
            if pending_tid:
                return pending_tid
        if create_if_missing:
            return uuid.uuid4().hex[:16]
        return ""

    def _query_clickhouse_tsv(
        self,
        sql: str,
        *,
        db_candidates: tuple[str, ...] = ("dolphin", "dolphin_prodgreen"),
        timeout: float = 5.0,
    ) -> tuple[str, str]:
        """Run a small ClickHouse HTTP query and return (raw_text, db_used)."""
        import base64 as _b64

        auth = "Basic " + _b64.b64encode(b"dolphin:dolphin_ch_2026").decode()
        last_exc: Exception | None = None
        for db in db_candidates:
            try:
                req = urllib.request.Request(
                    f"http://localhost:8123/?database={db}",
                    data=sql.encode(),
                    headers={"Authorization": auth},
                )
                with urllib.request.urlopen(req, timeout=timeout) as r:
                    return r.read().decode().strip(), db
            except Exception as exc:
                last_exc = exc
        raise last_exc or RuntimeError("ClickHouse query failed")

    def _parse_capital_blob(self, raw, source: str) -> tuple[float, dict] | None:
        """Parse a HZ/JSON state blob and validate the capital payload."""
        if not raw:
            return None
        try:
            data = json.loads(raw) if isinstance(raw, str) else (raw if isinstance(raw, dict) else {})
            capital = float(data.get("capital", 0) or 0)
            if capital >= 1.0 and math.isfinite(capital):
                return capital, data
            log(f"  restore candidate rejected from {source}: capital={capital!r}")
        except Exception as exc:
            log(f"  restore candidate parse failed from {source}: {exc}")
        return None

    def _parse_timestamp_seconds(self, value) -> float | None:
        """Parse epoch/ISO timestamps into UTC epoch seconds."""
        if value is None:
            return None
        try:
            if isinstance(value, (int, float)):
                ts = float(value)
            elif isinstance(value, str):
                text = value.strip()
                if not text:
                    return None
                try:
                    ts = float(text)
                except ValueError:
                    dt = datetime.fromisoformat(text.replace("Z", "+00:00"))
                    if dt.tzinfo is None:
                        dt = dt.replace(tzinfo=timezone.utc)
                    ts = dt.timestamp()
            else:
                return None
            if not math.isfinite(ts):
                return None
            # Accept millisecond epochs as well.
            if ts > 1.0e12:
                ts /= 1000.0
            return ts if ts > 0 else None
        except Exception:
            return None

    def _extract_state_timestamp(self, blob: dict) -> float | None:
        """Extract the best timestamp from a state blob."""
        if not isinstance(blob, dict):
            return None
        for key in ("updated_at", "timestamp", "ts", "iso"):
            if key not in blob:
                continue
            parsed = self._parse_timestamp_seconds(blob.get(key))
            if parsed is not None:
                return parsed
        return None

    def _mark_restore_failure(self, reason: str) -> None:
        """Mark restore as failed and force the trader into halt mode."""
        self._restore_failed = True
        self._restore_failure_reason = reason
        try:
            with self.eng_lock:
                if self.eng is not None:
                    self.eng.regime_dd_halt = True
                    self.eng._day_posture = "HIBERNATE"
        except Exception:
            pass
        log(f"RESTORE HALT: {reason}")

    def _restore_capital_from_legacy_checkpoint(self) -> bool:
        """Legacy escape hatch for the old scalar checkpoint path."""
        if not _env_bool("DOLPHIN_ALLOW_LEGACY_CAPITAL_CHECKPOINT", False):
            return False

        def _try_load(raw, source):
            parsed = self._parse_capital_blob(raw, source)
            if parsed is None:
                return False
            capital, _ = parsed
            self.eng.capital = capital
            self._restore_source = source
            log(f"  Capital restored from legacy {source}: ${capital:,.2f}")
            return True

        try:
            raw = self.state_map.blocking().get("capital_checkpoint")
            if _try_load(raw, "HZ capital_checkpoint"):
                return True
        except Exception as e:
            log(f"  capital HZ legacy restore failed: {e}")

        try:
            if CAPITAL_DISK_CHECKPOINT.exists():
                raw = CAPITAL_DISK_CHECKPOINT.read_text()
                if _try_load(raw, "disk capital_checkpoint"):
                    return True
        except Exception as e:
            log(f"  capital disk legacy restore failed: {e}")
        return False

    def _restore_capital_from_state(self) -> bool:
        """Restore capital from live HZ state or ledger-backed snapshots."""
        parsed_state = {}
        for key, label in (
            ("latest_nautilus", "HZ latest_nautilus"),
            ("engine_snapshot", "HZ engine_snapshot"),
        ):
            try:
                raw = self.state_map.blocking().get(key)
            except Exception as e:
                log(f"  capital {key} read failed: {e}")
                raw = None
            parsed = self._parse_capital_blob(raw, label)
            if parsed is not None:
                capital, blob = parsed
                parsed_state[key] = (
                    label,
                    capital,
                    blob,
                    self._extract_state_timestamp(blob),
                )

        day_key = datetime.now(timezone.utc).strftime('%Y-%m-%d')
        if self.pnl_map is not None:
            try:
                raw = self.pnl_map.blocking().get(day_key)
            except Exception as e:
                log(f"  capital pnl_map[{day_key}] read failed: {e}")
                raw = None
            parsed = self._parse_capital_blob(raw, f"HZ pnl[{day_key}]")
            if parsed is not None:
                capital, blob = parsed
                parsed_state["pnl_day"] = (
                    f"HZ pnl[{day_key}]",
                    capital,
                    blob,
                    self._extract_state_timestamp(blob),
                )

        if parsed_state:
            restore_tol = max(0.0001, _safe_float(os.environ.get("DOLPHIN_CAPITAL_RESTORE_TOL_PCT"), 0.002))
            stale_lag_s = max(0.0, _safe_float(os.environ.get("DOLPHIN_CAPITAL_SEED_STALE_LAG_SEC"), 180.0))
            force_latest_seed = _env_bool("DOLPHIN_FORCE_LATEST_NAUTILUS_RESTORE", False)

            def _mismatch(a: float, b: float) -> bool:
                return abs(a - b) > max(1.0, abs(a) * restore_tol)

            # Common-sense restore order:
            # 1) latest_nautilus = researched replay seed / operator-confirmed seed
            # 2) daily pnl map = corroborating capital sensor
            # 3) engine_snapshot = live observation only
            if "latest_nautilus" in parsed_state:
                label, capital, latest_blob, latest_ts = parsed_state["latest_nautilus"]
                reject_latest = False
                reject_details: list[str] = []
                mismatch_details: list[str] = []

                if "pnl_day" in parsed_state:
                    pnl_label, pnl_capital, _, pnl_ts = parsed_state["pnl_day"]
                    if _mismatch(pnl_capital, capital):
                        mismatch_details.append(
                            f"{pnl_label} ${pnl_capital:,.2f}"
                        )
                        if not force_latest_seed:
                            if latest_ts is None and pnl_ts is not None:
                                reject_latest = True
                                reject_details.append(f"{pnl_label} has timestamp, latest_nautilus does not")
                            elif latest_ts is not None and pnl_ts is not None and latest_ts + stale_lag_s < pnl_ts:
                                reject_latest = True
                                reject_details.append(
                                    f"{pnl_label} is newer by {pnl_ts - latest_ts:.1f}s"
                                )
                if "engine_snapshot" in parsed_state:
                    engine_label, engine_capital, _, engine_ts = parsed_state["engine_snapshot"]
                    if _mismatch(engine_capital, capital):
                        mismatch_details.append(
                            f"{engine_label} ${engine_capital:,.2f}"
                        )
                        if not force_latest_seed:
                            if latest_ts is None and engine_ts is not None:
                                reject_latest = True
                                reject_details.append(f"{engine_label} has timestamp, latest_nautilus does not")
                            elif latest_ts is not None and engine_ts is not None and latest_ts + stale_lag_s < engine_ts:
                                reject_latest = True
                                reject_details.append(
                                    f"{engine_label} is newer by {engine_ts - latest_ts:.1f}s"
                                )

                if reject_latest:
                    detail = "; ".join(reject_details) if reject_details else "freshness/consistency guard"
                    log(
                        "  Capital seed mismatch: ignoring stale latest_nautilus "
                        f"${capital:,.2f} ({detail})"
                    )
                else:
                    self.eng.capital = capital
                    self._restore_source = label
                    if mismatch_details:
                        log(
                            "  Capital sensor mismatch: preferring latest_nautilus "
                            f"${capital:,.2f} over " + ", ".join(mismatch_details)
                        )
                    log(f"  Capital restored from {label}: ${capital:,.2f}")
                    return True

            if "pnl_day" in parsed_state and "engine_snapshot" in parsed_state:
                pnl_label, pnl_capital, _, pnl_ts = parsed_state["pnl_day"]
                eng_label, eng_capital, _, eng_ts = parsed_state["engine_snapshot"]
                if _mismatch(pnl_capital, eng_capital):
                    if pnl_ts is not None and eng_ts is not None:
                        if eng_ts > pnl_ts:
                            log(
                                "  Capital sensor mismatch: preferring fresher engine_snapshot "
                                f"${eng_capital:,.2f} over {pnl_label} ${pnl_capital:,.2f}"
                            )
                            self.eng.capital = eng_capital
                            self._restore_source = eng_label
                            log(f"  Capital restored from {eng_label}: ${eng_capital:,.2f}")
                            return True
                    elif eng_ts is not None and pnl_ts is None:
                        log(
                            "  Capital sensor mismatch: preferring timestamped engine_snapshot "
                            f"${eng_capital:,.2f} over untimestamped {pnl_label} ${pnl_capital:,.2f}"
                        )
                        self.eng.capital = eng_capital
                        self._restore_source = eng_label
                        log(f"  Capital restored from {eng_label}: ${eng_capital:,.2f}")
                        return True

            if "pnl_day" in parsed_state:
                label, capital, _, _ = parsed_state["pnl_day"]
                self.eng.capital = capital
                self._restore_source = label
                log(f"  Capital restored from {label}: ${capital:,.2f}")
                return True

            label, capital, _, _ = parsed_state["engine_snapshot"]
            self.eng.capital = capital
            self._restore_source = label
            log(f"  Capital restored from {label}: ${capital:,.2f}")
            return True

        for sql, label in (
            (
                "SELECT ts, capital, trades_executed, posture, phase "
                "FROM status_snapshots ORDER BY ts DESC LIMIT 1 FORMAT TabSeparated",
                "status_snapshots",
            ),
            (
                "SELECT ts, capital_after, capital_before, pnl, exit_reason, trade_id "
                "FROM trade_events "
                "WHERE strategy='blue' AND capital_after > 0 "
                "ORDER BY ts DESC LIMIT 1 FORMAT TabSeparated",
                "trade_events",
            ),
        ):
            try:
                raw, db = self._query_clickhouse_tsv(sql)
                if not raw:
                    continue
                cols = raw.split("\t")
                capital = None
                if label == "status_snapshots" and len(cols) >= 2:
                    capital = float(cols[1])
                elif label == "trade_events" and len(cols) >= 4:
                    cap_after = float(cols[1])
                    cap_before = float(cols[2])
                    pnl = float(cols[3])
                    expected = cap_before + pnl
                    if math.isfinite(cap_after) and math.isfinite(expected):
                        if abs(cap_after - expected) <= max(1.0, abs(expected) * 0.002):
                            capital = cap_after
                        else:
                            log(
                                f"  restore candidate rejected from {db}.{label}: "
                                f"capital_after={cap_after:.2f} expected={expected:.2f} "
                                f"exit_reason={cols[4] if len(cols) > 4 else ''}"
                            )
                if capital is not None and math.isfinite(capital) and capital >= 1.0:
                    self.eng.capital = capital
                    self._restore_source = f"{db}.{label}"
                    log(f"  Capital restored from {db}.{label}: ${capital:,.2f}")
                    return True
            except Exception as e:
                log(f"  capital {label} replay failed: {e}")

        if self._restore_capital_from_legacy_checkpoint():
            return True

        self._mark_restore_failure("no sane capital source found (HZ state and ledger replay unavailable)")
        return False

    # ── CH position-state persistence ─────────────────────────────────────────

    def _ps_write_open(self, tid: str, entry: dict):
        """Persist OPEN row to position_state on entry. Fire-and-forget via ch_put."""
        try:
            ch_put("position_state", {
                "ts":          entry['entry_ts'],
                "trade_id":    tid,
                "asset":       entry['asset'],
                "direction":   -1 if entry['side'] == 'SHORT' else 1,
                "entry_price": entry['entry_price'],
                "quantity":    entry['quantity'],
                "notional":    round(entry['quantity'] * entry['entry_price'], 4),
                "leverage":    entry['leverage'],
                "bucket_id":   int(getattr(self, "_bucket_assignments", {}).get(entry['asset'], -1)),
                "entry_bar":   self.bar_idx,
                "status":      "OPEN",
                "exit_reason": "",
                "pnl":         0.0,
                "bars_held":   0,
            })
        except Exception as e:
            log(f"  position_state OPEN write failed: {e}")

    def _ps_write_closed(self, tid: str, pending: dict, x: dict):
        """Persist CLOSED row to position_state on exit (supersedes OPEN row via ReplacingMergeTree)."""
        try:
            ch_put("position_state", {
                "ts":          _ch_ts_us(),
                "trade_id":    tid,
                "asset":       pending.get('asset', ''),
                "direction":   -1 if pending.get('side') == 'SHORT' else 1,
                "entry_price": pending.get('entry_price', 0.0),
                "quantity":    pending.get('quantity', 0.0),
                "notional":    round(pending.get('quantity', 0.0) * pending.get('entry_price', 0.0), 4),
                "leverage":    pending.get('leverage', 0.0),
                "bucket_id":   int(getattr(self, "_bucket_assignments", {}).get(pending.get('asset', ''), -1)),
                "entry_bar":   0,
                "status":      "CLOSED",
                "exit_reason": str(x.get('reason', 'UNKNOWN')),
                "pnl":         float(x.get('net_pnl', 0) or 0),
                "bars_held":   int(x.get('bars_held', 0) or 0),
            })
        except Exception as e:
            log(f"  position_state CLOSED write failed: {e}")

    def _restore_position_state(self):
        """On startup: check CH for an OPEN position and restore engine state."""
        try:
            import urllib.request, base64 as _b64
            # IMPORTANT:
            # Never filter status='OPEN' first, otherwise stale historical OPEN rows
            # can be resurrected forever even after a newer CLOSED row exists.
            # Resolve latest row per trade_id first, then keep only currently-OPEN.
            sql = (
                "SELECT trade_id, asset, direction, entry_price, quantity, "
                "notional, leverage, bucket_id, bars_held "
                "FROM ("
                "  SELECT "
                "    trade_id, "
                "    argMax(asset, ts) AS asset, "
                "    argMax(direction, ts) AS direction, "
                "    argMax(entry_price, ts) AS entry_price, "
                "    argMax(quantity, ts) AS quantity, "
                "    argMax(notional, ts) AS notional, "
                "    argMax(leverage, ts) AS leverage, "
                "    argMax(bucket_id, ts) AS bucket_id, "
                "    argMax(bars_held, ts) AS bars_held, "
                "    argMax(status, ts) AS status, "
                "    argMax(ts, ts) AS last_ts "
                "  FROM dolphin.position_state "
                "  GROUP BY trade_id"
                ") "
                "WHERE status = 'OPEN' "
                "ORDER BY last_ts DESC LIMIT 1 FORMAT TabSeparated"
            )
            req = urllib.request.Request(
                "http://localhost:8123/?database=dolphin",
                data=sql.encode(),
                headers={"Authorization": "Basic " +
                         _b64.b64encode(b"dolphin:dolphin_ch_2026").decode()})
            with urllib.request.urlopen(req, timeout=5) as r:
                row = r.read().decode().strip()
            if not row:
                log("  position_state: no open position to restore")
                return

            cols = row.split('\t')
            if len(cols) < 9:
                log(f"  position_state: unexpected row format: {row}")
                self._mark_restore_failure("position_state row malformed")
                return

            trade_id   = cols[0]
            asset      = cols[1]
            direction  = int(cols[2])
            entry_price = float(cols[3])
            quantity   = float(cols[4])
            notional   = float(cols[5])
            leverage   = float(cols[6])
            bucket_id  = int(cols[7])
            stored_bars = int(cols[8])

            if not trade_id.strip():
                self._mark_restore_failure("position_state row missing trade_id")
                return
            if not asset.strip():
                self._mark_restore_failure(f"position_state row missing asset for trade {trade_id}")
                return
            if direction not in (-1, 1):
                self._mark_restore_failure(f"position_state row invalid direction for trade {trade_id}: {direction}")
                return
            if not (math.isfinite(entry_price) and entry_price > 0):
                self._mark_restore_failure(f"position_state row invalid entry_price for trade {trade_id}: {entry_price}")
                return
            if not (math.isfinite(quantity) and quantity > 0):
                self._mark_restore_failure(f"position_state row invalid quantity for trade {trade_id}: {quantity}")
                return
            if not (math.isfinite(notional) and notional > 0):
                self._mark_restore_failure(f"position_state row invalid notional for trade {trade_id}: {notional}")
                return
            if not (math.isfinite(leverage) and leverage > 0):
                self._mark_restore_failure(f"position_state row invalid leverage for trade {trade_id}: {leverage}")
                return
            if stored_bars < 0:
                self._mark_restore_failure(f"position_state row invalid bars_held for trade {trade_id}: {stored_bars}")
                return
            derived_notional = quantity * entry_price
            if math.isfinite(derived_notional) and derived_notional > 0:
                if abs(notional - derived_notional) > max(1.0, abs(derived_notional) * 0.01):
                    log(
                        "  position_state notional mismatch: "
                        f"stored={notional:.6f} derived={derived_notional:.6f} trade={trade_id} "
                        "— using derived value"
                    )
                    notional = derived_notional

            # Estimate entry_bar so MAX_HOLD countdown continues from where it left off
            restored_entry_bar = max(0, self.bar_idx - stored_bars)
            chain_recon = self._load_chain_ledger_state(trade_id)
            chain_meta = {}
            if chain_recon:
                chain_meta.update(chain_recon)
                nested_chain = chain_recon.get("chain")
                if isinstance(nested_chain, dict):
                    chain_meta.update(nested_chain)
            chain_seed_pending = {
                "asset": asset,
                "side": 'SHORT' if direction == -1 else 'LONG',
                "entry_price": entry_price,
                "quantity": quantity,
                "notional": notional,
                "notional_entry": notional,
                "leverage": leverage,
                "entry_bar": int(chain_meta.get("entry_bar", restored_entry_bar) if chain_recon else restored_entry_bar),
                "entry_ts": int(chain_meta.get("entry_ts", 0) or 0) if chain_recon else 0,
                "retraction_legs": int(chain_meta.get("retraction_legs", chain_meta.get("chain_seq", 0)) or 0) if chain_recon else 0,
                "realized_pnl_legs_total": float(chain_meta.get("realized_pnl_legs_total", 0.0) or 0.0) if chain_recon else 0.0,
            }
            try:
                chain_state = self._chain_state_from_reconstruction(trade_id, chain_seed_pending, chain_recon)
            except Exception as chain_err:
                self._mark_restore_failure(str(chain_err))
                return

            pos = NDPosition(
                trade_id    = trade_id,
                asset       = asset,
                direction   = direction,
                entry_price = entry_price,
                entry_bar   = restored_entry_bar,
                notional    = notional,
                leverage    = leverage,
                fraction    = notional / max(self.eng.capital * leverage, 1.0),
                entry_vel_div = 0.0,
                bucket_idx  = 0,   # signal-strength bucket (not KMeans); 0=safe default
                current_price = entry_price,
            )
            with self.eng_lock:
                self.eng.position = pos
                self.eng.exit_manager.setup_position(
                    trade_id, entry_price, direction, restored_entry_bar,
                )
                # NOTE: do NOT arm hibernate protect here.
                # _day_posture starts as 'APEX' — the posture sync block on the
                # first incoming scan will detect the APEX→HIBERNATE transition
                # and call _hibernate_protect_position() at the right moment.

            # Rebuild _pending_entries so the exit CH write fires correctly
            side = 'SHORT' if direction == -1 else 'LONG'
            self._pending_entries[trade_id] = {
                'trade_id':       trade_id,
                'asset':          asset,
                'side':           side,
                'entry_price':    entry_price,
                'quantity':       quantity,
                'notional':       float(quantity * entry_price),
                'notional_entry': float(quantity * entry_price),
                'leverage':       leverage,
                'vel_div_entry':  0.0,
                'boost_at_entry': 1.0,
                'beta_at_entry':  1.0,
                'posture':        'RESTORED',
                'entry_ts':       int(chain_meta.get("entry_ts", _ch_ts_us()) or _ch_ts_us()) if chain_recon else _ch_ts_us(),
                'entry_date':     (self.current_day or ''),
                'retraction_legs': int(chain_state.get("chain_seq", 0) or 0),
                'realized_pnl_legs_total': float(chain_state.get("realized_pnl_legs_total", 0.0) or 0.0),
                'chain_root_trade_id': chain_state.get("chain_root_trade_id", trade_id),
                'chain_head_leg_id': chain_state.get("chain_head_leg_id", f"{trade_id}:open"),
                'chain_prev_leg_id': chain_state.get("chain_prev_leg_id", ""),
                'chain_seq': int(chain_state.get("chain_seq", 0) or 0),
                'chain_token': chain_state.get("chain_token", ""),
                'chain_mode': chain_state.get("chain_mode", "LIVE"),
                'chain_version': int(chain_state.get("chain_version", 1) or 1),
                'chain_kind': chain_state.get("chain_kind", "ROOT"),
            }
            if self._v7_exit_engine is not None:
                try:
                    ctx = self._v7_exit_engine.make_context(
                        entry_price=entry_price,
                        entry_bar=restored_entry_bar,
                        side=1 if direction == -1 else 0,
                    )
                    self._v7_contexts[trade_id] = ctx
                    self._v7_decision_seq[trade_id] = 0
                except Exception as e:
                    log(f"  V7 live restore context failed: {e}")
            log(f"  position_state RESTORED: {asset} {side} entry={entry_price} "
                f"notional={notional:.0f} bars_held≈{stored_bars} trade={trade_id}")

        except Exception as e:
            log(f"  position_state restore error: {e}")
            self._mark_restore_failure(f"position_state restore error: {e}")

    def _hibernate_protect_position(self):
        """Arm per-bucket TP+SL instead of immediate HIBERNATE_HALT.

        Must be called under eng_lock with an open position.
        Sets stop_pct_override on the live exit_manager state so the position
        exits via FIXED_TP or STOP_LOSS rather than being force-closed.
        Records trade_id in _hibernate_protect_active so the exit path can
        re-label the reason and finalize posture once the position closes.
        """
        pos = self.eng.position
        if pos is None:
            return
        bucket = getattr(self, "_bucket_assignments", {}).get(pos.asset, 'default')
        sl_pct = _BUCKET_SL_PCT.get(bucket, _BUCKET_SL_PCT['default'])
        tp_pct = self.eng.exit_manager.fixed_tp_pct

        # Patch the live exit_manager state for this trade_id
        em_state = self.eng.exit_manager._positions.get(pos.trade_id)
        if em_state is not None:
            em_state['stop_pct_override'] = sl_pct
        else:
            # Position not registered in exit_manager (shouldn't happen, but be safe)
            log(f"  HIBERNATE_PROTECT: trade {pos.trade_id} not in exit_manager — arming anyway via re-setup")
            self.eng.exit_manager.setup_position(
                pos.trade_id, pos.entry_price, pos.direction, pos.entry_bar,
                stop_pct_override=sl_pct,
            )

        self._hibernate_protect_active = pos.trade_id
        log(f"HIBERNATE_PROTECT armed: {pos.asset} B{bucket} "
            f"SL={sl_pct*100:.2f}% TP={tp_pct*100:.2f}% trade={pos.trade_id}")

    def _connect_hz(self):
        log("Connecting to Hazelcast...")
        import hazelcast
        self.hz_client = hazelcast.HazelcastClient(cluster_name=HZ_CLUSTER, cluster_members=[HZ_HOST])
        self.features_map = self.hz_client.get_map("DOLPHIN_FEATURES")
        self.safety_map = self.hz_client.get_map("DOLPHIN_SAFETY")
        self.pnl_map = self.hz_client.get_map("DOLPHIN_PNL_BLUE")
        self.state_map = self.hz_client.get_map("DOLPHIN_STATE_BLUE")
        self.heartbeat_map = self.hz_client.get_map("DOLPHIN_HEARTBEAT")
        self.control_map = self.hz_client.get_map("DOLPHIN_CONTROL_PLANE")
        if self._advanced_sl is not None:
            try:
                self._advanced_sl.bind_hz(features_map=self.features_map, state_map=self.state_map)
                self._advanced_sl.publish_control_plane()
            except Exception:
                pass
        # Immediate heartbeat — prevents Cat1=0 during startup gap
        try:
            self.heartbeat_map.blocking().put('nautilus_flow_heartbeat', json.dumps({
                'ts': time.time(),
                'iso': datetime.now(timezone.utc).isoformat(),
                'phase': 'starting',
                'flow': 'nautilus_event_trader',
            }))
        except Exception:
            pass
        log("  Hz connected")

    def _heartbeat_loop(self):
        """Out-of-band heartbeat writer (independent of scan loop)."""
        while not self._heartbeat_stop.is_set():
            try:
                if self.heartbeat_map is not None:
                    hb = json.dumps({
                        'ts': time.time(),
                        'iso': datetime.now(timezone.utc).isoformat(),
                        'run_date': self.current_day,
                        'phase': 'trading',
                        'flow': 'nautilus_event_trader',
                    })
                    self.heartbeat_map.blocking().put('nautilus_flow_heartbeat', hb)
            except Exception as e:
                log(f"  Heartbeat loop put failed: {e}")
            self._heartbeat_stop.wait(10.0)
        
    def _read_posture(self):
        now = time.time()
        if now - self.posture_cache_time < 10:
            return self.cached_posture
        try:
            posture_raw = self.safety_map.blocking().get("latest") or self.safety_map.blocking().get("posture")
            if posture_raw:
                if isinstance(posture_raw, str):
                    try:
                        parsed = json.loads(posture_raw)
                        self.cached_posture = parsed.get("posture", posture_raw)
                    except (json.JSONDecodeError, AttributeError):
                        self.cached_posture = posture_raw
                else:
                    self.cached_posture = posture_raw.get("posture", "APEX")
            self.posture_cache_time = now
        except:
            pass
        return self.cached_posture
        
    def _rollover_day(self):
        today = datetime.now(timezone.utc).strftime('%Y-%m-%d')
        if today == self.current_day:
            return
        posture = self._read_posture()
        with self.eng_lock:
            if today != self.current_day:  # double-checked: only one thread calls begin_day
                if getattr(self, 'acb', None):
                    try:
                        exf_raw = self.features_map.blocking().get('exf_latest') if self.features_map else None
                        es_raw = self.features_map.blocking().get('latest_eigen_scan') if self.features_map else None
                        
                        exf_snapshot = json.loads(exf_raw) if isinstance(exf_raw, str) else (exf_raw or {})
                        eigen_scan = json.loads(es_raw) if isinstance(es_raw, str) else (es_raw or {})
                        
                        w750_vel = eigen_scan.get('w750_velocity', 0.0)
                        
                        if exf_snapshot:
                            self.acb.get_dynamic_boost_from_hz(
                                date_str=today, 
                                exf_snapshot=exf_snapshot,
                                w750_velocity=float(w750_vel) if w750_vel else None,
                                direction=self.trade_direction,
                            )
                            log(f"ACB: Pre-warmed cache for {today} from HZ")
                    except Exception as e:
                        log(f"ACB Rollover Error: {e}")
            
                self.eng.begin_day(today, posture=posture, direction=self.trade_direction)
                self.bar_idx = 0
                self.current_day = today
                log(
                    f"begin_day({today}) called with posture={posture} "
                    f"direction={_direction_label(self.trade_direction)}"
                )

    def _mark_retract_command_seen(self, command_id: str) -> None:
        if not command_id or command_id in self._processed_retract_set:
            return
        self._processed_retract_commands.append(command_id)
        self._processed_retract_set.add(command_id)

    def _build_retract_exit(self, *, trade_id: str, reason: str, bars_held: int, pnl_pct: float, net_pnl: float) -> dict:
        return {
            "trade_id": trade_id,
            "reason": reason,
            "bars_held": int(max(0, bars_held)),
            "pnl_pct": float(pnl_pct),
            "net_pnl": float(net_pnl),
        }

    def _chain_state_for_pending(
        self,
        trade_id: str,
        pending: dict,
        *,
        chain_mode: str = "LIVE",
        chain_head_leg_id: str | None = None,
        chain_prev_leg_id: str | None = None,
        chain_seq: int | None = None,
    ) -> dict:
        """Return the canonical linked-list state for the current open trade head."""
        seq = int(chain_seq if chain_seq is not None else pending.get("retraction_legs", 0) or 0)
        quantity = float(pending.get("quantity", 0.0) or 0.0)
        entry_price = float(pending.get("entry_price", 0.0) or 0.0)
        notional = float(pending.get("notional", pending.get("notional_entry", 0.0)) or 0.0)
        entry_bar = int(pending.get("entry_bar", 0) or 0)
        entry_ts = int(pending.get("entry_ts", 0) or 0)
        realized = float(pending.get("realized_pnl_legs_total", 0.0) or 0.0)
        return _build_chain_state(
            trade_id=str(trade_id or ""),
            asset=str(pending.get("asset", "") or ""),
            side=str(pending.get("side", "") or "SHORT"),
            entry_price=entry_price,
            quantity=quantity,
            notional=notional,
            entry_bar=entry_bar,
            entry_ts=entry_ts,
            retraction_legs=seq,
            realized_pnl_legs_total=realized,
            chain_root_trade_id=str(pending.get("chain_root_trade_id", trade_id) or trade_id),
            chain_head_leg_id=chain_head_leg_id or pending.get("chain_head_leg_id"),
            chain_prev_leg_id=chain_prev_leg_id if chain_prev_leg_id is not None else str(pending.get("chain_prev_leg_id", "") or ""),
            chain_mode=chain_mode,
        )

    def _load_chain_ledger_state(self, trade_id: str) -> dict | None:
        """Load the latest reconstruction payload for a trade, if ClickHouse is reachable."""
        try:
            import base64 as _b64
            escaped_tid = str(trade_id or "").replace("'", "''")
            sql = (
                "SELECT event_type, event_id, payload_json "
                "FROM dolphin.trade_reconstruction "
                f"WHERE trade_id = '{escaped_tid}' "
                "ORDER BY ts DESC LIMIT 1 FORMAT JSONEachRow"
            )
            req = urllib.request.Request(
                "http://localhost:8123/?database=dolphin",
                data=sql.encode(),
                headers={"Authorization": "Basic " +
                         _b64.b64encode(b"dolphin:dolphin_ch_2026").decode()},
            )
            with urllib.request.urlopen(req, timeout=5) as r:
                raw = r.read().decode().strip()
            if not raw:
                return None
            row = json.loads(raw.splitlines()[0])
            payload = json.loads(row.get("payload_json", "{}") or "{}")
            payload["event_type"] = row.get("event_type", "")
            payload["event_id"] = row.get("event_id", "")
            return payload
        except Exception:
            return None

    def _chain_state_from_reconstruction(self, trade_id: str, pending: dict, recon: dict | None) -> dict:
        """Merge reconstruction payload chain hints with the current live state."""
        chain_data = {}
        seq = 0
        prev_leg_id = ""
        head_leg_id = f"{trade_id}:open"
        chain_mode = "LEGACY"
        if recon:
            chain_data.update(recon)
            nested = recon.get("chain")
            if isinstance(nested, dict):
                chain_data.update(nested)
            seq = int(chain_data.get("chain_seq", chain_data.get("retraction_legs", 0)) or 0)
            prev_leg_id = str(chain_data.get("chain_prev_leg_id", "") or "")
            head_leg_id = str(chain_data.get("chain_head_leg_id", "") or head_leg_id)
            chain_mode = str(chain_data.get("chain_mode", "LIVE") or "LIVE")
            if "chain_token" not in chain_data:
                chain_mode = "LEGACY_REBUILT"
        chain = self._chain_state_for_pending(
            trade_id,
            pending,
            chain_mode=chain_mode,
            chain_head_leg_id=head_leg_id,
            chain_prev_leg_id=prev_leg_id,
            chain_seq=seq,
        )
        if chain_data.get("chain_token"):
            expected = str(chain_data.get("chain_token", "") or "")
            if expected != chain.get("chain_token"):
                raise ValueError(
                    f"chain token mismatch for trade {trade_id}: "
                    f"stored={expected[:12]} derived={chain.get('chain_token','')[:12]}"
                )
        return chain

    def _apply_internal_retract(self, cmd: dict, prices_dict: dict) -> tuple[dict | None, str]:
        """Apply partial retraction on in-memory BLUE position; returns (forced_exit, status)."""
        with self.eng_lock:
            pos = getattr(self.eng, "position", None)
            if pos is None:
                return None, "NO_POSITION"
            tid = str(getattr(pos, "trade_id", "") or "")
            if not tid:
                return None, "NO_TRADE_ID"
            req_tid = str(cmd.get("trade_id", "") or "").strip()
            if req_tid and req_tid != tid:
                return None, f"TRADE_MISMATCH open={tid} cmd={req_tid}"
            pending = self._pending_entries.get(tid) or {}
            side = str(pending.get("side", "SHORT") or "SHORT").upper()
            entry_price = float(pending.get("entry_price", getattr(pos, "entry_price", 0.0)) or 0.0)
            if entry_price <= 0:
                return None, "BAD_ENTRY_PRICE"
            open_notional = float(getattr(pos, "notional", 0.0) or 0.0)
            if open_notional <= 0:
                return None, "ZERO_NOTIONAL"
            frac = float(cmd.get("fraction", 0.0) or 0.0)
            if not (0.0 < frac <= 1.0):
                return None, "BAD_FRACTION"
            expected_chain = self._chain_state_for_pending(tid, pending)
            cmd_chain_token = str(cmd.get("chain_token", "") or "").strip()
            cmd_chain_head = str(cmd.get("chain_head_leg_id", "") or "").strip()
            cmd_chain_root = str(cmd.get("chain_root_trade_id", "") or "").strip()
            cmd_chain_seq = int(cmd.get("chain_seq", expected_chain["chain_seq"]) or expected_chain["chain_seq"])
            if not cmd_chain_token or not cmd_chain_head or not cmd_chain_root:
                return None, "NO_CHAIN_LINK"
            if cmd_chain_root != expected_chain["chain_root_trade_id"]:
                return None, f"CHAIN_ROOT_MISMATCH expected={expected_chain['chain_root_trade_id']} cmd={cmd_chain_root}"
            if cmd_chain_head != expected_chain["chain_head_leg_id"] or cmd_chain_token != expected_chain["chain_token"]:
                return None, (
                    f"CHAIN_MISMATCH head={expected_chain['chain_head_leg_id']} "
                    f"seq={expected_chain['chain_seq']} token={expected_chain['chain_token'][:12]}"
                )
            if cmd_chain_seq != expected_chain["chain_seq"]:
                return None, (
                    f"CHAIN_SEQ_MISMATCH expected={expected_chain['chain_seq']} cmd={cmd_chain_seq}"
                )
            reduce_notional = min(open_notional, open_notional * frac)
            if reduce_notional <= 0.0:
                return None, "ZERO_REDUCE_NOTIONAL"
            current_price = float(prices_dict.get(pos.asset, getattr(pos, "current_price", entry_price)) or entry_price)
            if current_price <= 0:
                current_price = entry_price
            direction = -1.0 if side == "SHORT" else 1.0
            pnl_pct_now = direction * ((current_price - entry_price) / entry_price)
            net_pnl_leg = pnl_pct_now * reduce_notional
            bars_held = max(0, int(self.bar_idx - int(pending.get("entry_bar", max(0, self.bar_idx - 1)) or max(0, self.bar_idx - 1))))
            self.eng.capital = float(getattr(self.eng, "capital", 0.0) or 0.0) + net_pnl_leg
            remaining_notional = max(0.0, open_notional - reduce_notional)
            pos.notional = remaining_notional
            pos.current_price = current_price
            pos.pnl_pct = pnl_pct_now
            pending.setdefault("notional_entry", float(pending.get("notional", open_notional) or open_notional))
            pending["notional"] = remaining_notional
            pending["quantity"] = round((remaining_notional / entry_price), 6) if entry_price > 0 else 0.0
            pending["retraction_legs"] = int(pending.get("retraction_legs", 0) or 0) + 1
            pending["realized_pnl_legs_total"] = float(pending.get("realized_pnl_legs_total", 0.0) or 0.0) + net_pnl_leg
            leg_seq = int(pending["retraction_legs"])
            leg_id = f"{tid}:x{leg_seq:03d}"
            chain_state = self._chain_state_for_pending(
                tid,
                {
                    **pending,
                    "chain_root_trade_id": expected_chain["chain_root_trade_id"],
                    "chain_prev_leg_id": expected_chain["chain_head_leg_id"],
                    "chain_head_leg_id": leg_id,
                    "chain_mode": "LIVE",
                },
                chain_mode="LIVE",
                chain_head_leg_id=leg_id,
                chain_prev_leg_id=expected_chain["chain_head_leg_id"],
                chain_seq=leg_seq,
            )
            self._pending_entries[tid] = pending
            pending.update(chain_state)
            current_bars_held = bars_held
            entry_bar = int(pending.get("entry_bar", max(0, self.bar_idx - current_bars_held)) or max(0, self.bar_idx - current_bars_held))
            ch_put("position_state", {
                "ts": _ch_ts_us(),
                "trade_id": tid,
                "asset": str(getattr(pos, "asset", pending.get("asset", ""))),
                "direction": -1 if side == "SHORT" else 1,
                "entry_price": entry_price,
                "quantity": pending["quantity"],
                "notional": round(remaining_notional, 4),
                "leverage": pending.get("leverage", getattr(pos, "leverage", 0.0)),
                "bucket_id": int(getattr(self, "_bucket_assignments", {}).get(pending.get("asset", ""), -1)),
                "entry_bar": entry_bar,
                "status": "OPEN",
                "exit_reason": "",
                "pnl": float(pending.get("realized_pnl_legs_total", 0.0) or 0.0),
                "bars_held": current_bars_held,
            })
            ch_put("trade_exit_legs", {
                "ts": _ch_ts_us(),
                "date": str(pending.get("entry_date", self.current_day or "")),
                "strategy": "blue",
                "trade_id": tid,
                "chain_root_trade_id": str(chain_state.get("chain_root_trade_id", tid) or tid),
                "chain_head_leg_id": str(chain_state.get("chain_head_leg_id", leg_id) or leg_id),
                "chain_prev_leg_id": str(chain_state.get("chain_prev_leg_id", "") or ""),
                "chain_seq": int(chain_state.get("chain_seq", leg_seq) or leg_seq),
                "chain_token": str(chain_state.get("chain_token", "") or ""),
                "chain_mode": str(chain_state.get("chain_mode", "LIVE") or "LIVE"),
                "exit_leg_id": leg_id,
                "exit_seq": leg_seq,
                "command_id": str(cmd.get("command_id", "")),
                "source": str(cmd.get("source", "internal")),
                "reason": str(cmd.get("reason", "RETRACT")),
                "asset": str(getattr(pos, "asset", pending.get("asset", ""))),
                "side": side,
                "entry_price": entry_price,
                "exit_price": current_price,
                "fraction": frac,
                "exit_notional": reduce_notional,
                "remaining_notional": remaining_notional,
                "remaining_qty": (remaining_notional / entry_price) if entry_price > 0 else 0.0,
                "pnl_pct_leg": pnl_pct_now,
                "pnl_leg": net_pnl_leg,
                "pnl_realized_total": float(pending.get("realized_pnl_legs_total", 0.0) or 0.0),
                "bars_held": bars_held,
            })
            ch_put("trade_reconstruction", {
                "ts": _ch_ts_us(),
                "trade_id": tid,
                "event_type": "PARTIAL_EXIT",
                "event_id": leg_id,
                "payload_json": json.dumps({
                    "command": cmd,
                    "entry_price": entry_price,
                    "exit_price": current_price,
                    "exit_notional": reduce_notional,
                    "remaining_notional": remaining_notional,
                    "pnl_pct_leg": pnl_pct_now,
                    "pnl_leg": net_pnl_leg,
                    "pnl_realized_total": float(pending.get("realized_pnl_legs_total", 0.0) or 0.0),
                    "bar_idx": int(self.bar_idx),
                    "chain": chain_state,
                }),
            })
            if remaining_notional <= 1e-9:
                self.eng.position = None
                try:
                    self.eng.exit_manager._positions.pop(tid, None)
                except Exception:
                    pass
                total_realized = float(pending.get("realized_pnl_legs_total", 0.0) or 0.0)
                denom = max(float(pending.get("notional_entry", open_notional) or open_notional), 1e-12)
                forced = self._build_retract_exit(
                    trade_id=tid,
                    reason=str(cmd.get("reason", "RETRACT_FULL")),
                    bars_held=bars_held,
                    pnl_pct=total_realized / denom,
                    net_pnl=total_realized,
                )
                return forced, "FULL_CLOSE"
            return None, "PARTIAL_OK"

    def _process_runtime_commands(self, prices_dict: dict) -> dict | None:
        """Drain BLUE runtime commands from control plane and apply retractions."""
        if self.control_map is None:
            return None
        key = "blue_runtime_commands"
        try:
            raw = self.control_map.blocking().get(key)
            if not raw:
                return None
            queue = json.loads(raw) if isinstance(raw, str) else list(raw)
            if not isinstance(queue, list) or not queue:
                return None
            self.control_map.blocking().put(key, json.dumps([]))
        except Exception as e:
            log(f"RUNTIME_CMD read failed: {e}")
            return None
        forced_exit = None
        for cmd in queue:
            if not isinstance(cmd, dict):
                continue
            cid = str(cmd.get("command_id", "") or "")
            if cid and cid in self._processed_retract_set:
                ch_put("hotkey_audit", {
                    "ts": int(time.time() * 1000),
                    "hotkey": "RETRACT_REPLAY",
                    "request_json": json.dumps(cmd, default=str),
                    "result": "IDEMPOTENT_REPLAY",
                    "effect_json": json.dumps({}, default=str),
                })
                continue
            if str(cmd.get("action", "") or "").upper() != "RETRACT":
                continue
            fx, status = self._apply_internal_retract(cmd, prices_dict)
            self._mark_retract_command_seen(cid)
            ch_put("hotkey_audit", {
                "ts": int(time.time() * 1000),
                "hotkey": "RETRACT",
                "request_json": json.dumps(cmd, default=str),
                "result": status,
                "effect_json": json.dumps({"forced_exit": bool(fx)}, default=str),
            })
            if fx is not None:
                forced_exit = fx
        return forced_exit
            
    def _compute_vol_ok(self, scan):
        assets = scan.get('assets', [])
        prices = scan.get('asset_prices', [])
        if not assets or not prices:
            return True
        prices_dict = dict(zip(assets, prices))
        btc_price = prices_dict.get('BTCUSDT')
        if btc_price is None:
            return True
        self.btc_prices.append(float(btc_price))
        if len(self.btc_prices) < BTC_VOL_WINDOW:
            return True
        import numpy as np
        arr = np.array(self.btc_prices)
        dvol = float(np.std(np.diff(arr) / arr[:-1]))
        return dvol > float(self.vol_p60_threshold)
        
    @staticmethod
    def _normalize_ng7(scan: dict) -> dict:
        """
        Promote NG7-format scan to NG5-compatible flat dict.
        NG7 embeds eigenvalue windows and prices inside result{} — the engine
        expects flat top-level fields.  Mapping derived from continuous_convert.py:
          vel_div       = w50_velocity − w750_velocity  (fast minus slow eigenvalue velocity)
          w50_velocity  = multi_window_results["50"].tracking_data.lambda_max_velocity
          w750_velocity = multi_window_results["750"].tracking_data.lambda_max_velocity
          assets        = sorted(current_prices.keys()), BTCUSDT always last
        """
        result = scan.get('result') or {}
        mw     = result.get('multi_window_results') or {}

        def _vel(win):
            v = (mw.get(str(win)) or {}).get('tracking_data', {}).get('lambda_max_velocity')
            try:
                f = float(v)
                return f if math.isfinite(f) else 0.0
            except (TypeError, ValueError):
                return 0.0

        v50  = _vel(50)
        v150 = _vel(150)
        v750 = _vel(750)

        cp     = (result.get('pricing_data') or {}).get('current_prices') or {}
        assets = [a for a in cp if a != 'BTCUSDT']
        if 'BTCUSDT' in cp:
            assets.append('BTCUSDT')   # BTC always last — matches NG5/Arrow convention
        prices = [float(cp[a]) for a in assets]

        instability = float((result.get('regime_prediction') or {})
                            .get('instability_score') or 0.0)

        return {
            **scan,
            'vel_div':        v50 - v750,
            'w50_velocity':   v50,
            'w750_velocity':  v750,
            'assets':         assets,
            'asset_prices':   prices,
            'instability_50': instability,
        }

    def on_scan(self, event):
        """Reactor-thread entry point — dispatches immediately to worker thread."""
        if self._restore_failed or not event.value:
            return
        listener_time = time.time()
        self._scan_executor.submit(self._process_scan, event, listener_time)

    def _process_scan(self, event, listener_time):
        try:
            if self._restore_failed or not event.value:
                return

            scan = json.loads(event.value) if isinstance(event.value, str) else event.value

            # Normalise NG7 format → NG5-compatible flat dict before any field access
            if scan.get('version') == 'NG7':
                scan = self._normalize_ng7(scan)

            scan_number = int(scan.get('scan_number') or 0)

            # Dedup: scan_number is authoritative (monotonically increasing).
            # file_mtime / timestamp are unreliable across NG7 restart probes.
            with self._dedup_lock:
                if scan_number > 0 and scan_number <= self.last_scan_number:
                    return
                self.last_scan_number = scan_number
            self.scans_processed += 1
            
            self._rollover_day()
            
            assets = scan.get('assets') or []
            if assets and not self.ob_assets:
                self._wire_obf(assets)

            prices = scan.get('asset_prices') or []
            if assets and prices and len(assets) != len(prices):
                log(f"WARN scan #{scan_number}: assets/prices mismatch "
                    f"({len(assets)}≠{len(prices)}) — dropped")
                return
            prices_dict = dict(zip(assets, prices)) if assets and prices else {}
            # Remove stablecoins — they should never be selected as a trade asset
            for sym in _STABLECOIN_SYMBOLS:
                prices_dict.pop(sym, None)

            self._record_bounce_prices(prices_dict)

            vol_ok = self._compute_vol_ok(scan)

            vel_div = float(scan.get('vel_div') or 0.0)
            if not math.isfinite(vel_div):
                log(f"WARN scan #{scan_number}: non-finite vel_div={vel_div} — clamped to 0.0")
                vel_div = 0.0

            v50_vel  = float(scan.get('w50_velocity')  or 0.0)
            v750_vel = float(scan.get('w750_velocity') or 0.0)
            if not math.isfinite(v50_vel):  v50_vel  = 0.0
            if not math.isfinite(v750_vel): v750_vel = 0.0
            self.last_w750_vel = v750_vel

            # Feed live OB data into OBF engine for this bar (AGENT_SPEC_OBF_LIVE_SWITCHOVER)
            if self.ob_eng is not None and self.ob_assets:
                self.ob_eng.step_live(self.ob_assets, self.bar_idx)

            # Live posture sync — update engine posture + regime_dd_halt together
            posture_now = self._read_posture()
            with self.eng_lock:
                prev_posture = getattr(self.eng, '_day_posture', 'APEX')
                if posture_now != prev_posture:
                    if posture_now in ('TURTLE', 'HIBERNATE'):
                        self.eng.regime_dd_halt = True  # always block new entries
                        if posture_now == 'HIBERNATE' and self.eng.position is not None:
                            open_tid = str(getattr(self.eng.position, "trade_id", "") or "")
                            if not open_tid:
                                self._mark_restore_failure("HIBERNATE posture with open position missing trade_id")
                                return
                            if open_tid not in self._pending_entries:
                                self._mark_restore_failure(
                                    f"HIBERNATE posture with open position missing pending entry: {open_tid}"
                                )
                                return
                        if (posture_now == 'HIBERNATE'
                                and self.eng.position is not None
                                and not self._hibernate_protect_active):
                            # Position in flight: arm TP+SL instead of letting
                            # _manage_position() fire HIBERNATE_HALT next bar.
                            # _day_posture stays at prev value — no HALT fires.
                            self._hibernate_protect_position()
                        else:
                            self.eng._day_posture = posture_now
                            log(f"POSTURE_SYNC: {posture_now} — halt set")
                    else:
                        self.eng._day_posture = posture_now
                        self.eng.regime_dd_halt = False
                        if self._hibernate_protect_active:
                            log(f"POSTURE_SYNC: {posture_now} — posture recovered, clearing protect mode")
                            self._hibernate_protect_active = None
                        else:
                            log(f"POSTURE_SYNC: {posture_now} — halt lifted")

            # EsoF value gate — exposure only, no alpha or selection changes.
            self._sync_esof_size_gate()
            self._sync_tp_threshold()
            self._sync_sc_threshold_advisor(scan_number=scan_number, vel_div=vel_div)
            self._sync_sc_gauge_advisor(scan_number=scan_number, vel_div=vel_div)
            self._apply_runtime_direction()
            if self._market_state_runtime is not None:
                try:
                    self._market_state_runtime.update_scan_state(
                        scan_payload=scan,
                        prices_dict=prices_dict,
                        scan_number=scan_number,
                        vel_div=vel_div,
                        v50_vel=v50_vel,
                        v750_vel=v750_vel,
                        vol_ok=vol_ok,
                        posture=posture_now,
                        exf_snapshot=getattr(self, "_last_exf", {}) or {},
                        esof_payload=self._read_esof_payload(),
                        top_k_assets=5,
                    )
                except Exception as e:
                    log(f"  MarketStateRuntime scan update failed: {e}")

            if self.eng.position is not None and prices_dict:
                prices_dict = self._inject_obf_midprice(prices_dict)

            step_start = time.time()
            with self.eng_lock:
                result = self.eng.step_bar(
                    bar_idx=self.bar_idx, vel_div=vel_div, prices=prices_dict,
                    vol_regime_ok=vol_ok, v50_vel=v50_vel, v750_vel=v750_vel
                )
                self.bar_idx += 1
            scan_to_fill_ms = (time.time() - listener_time) * 1000
            step_bar_ms     = (time.time() - step_start) * 1000
            log(f"LATENCY scan #{scan_number}: scan→fill={scan_to_fill_ms:.1f}ms step_bar={step_bar_ms:.1f}ms vel_div={vel_div:.5f}")

            ch_put("eigen_scans", {
                "ts":              _ch_ts_us(),
                "scan_number":     scan_number,
                "scan_uuid":       str(scan.get("scan_uuid") or ""),
                "vel_div":         vel_div,
                "w50_velocity":    v50_vel,
                "w750_velocity":   v750_vel,
                "instability_50":  float(scan.get("instability_50") or 0.0),
                "scan_to_fill_ms": scan_to_fill_ms,
                "step_bar_ms":     step_bar_ms,
            })

            if result.get('entry'):
                self.trades_executed += 1
                e = result['entry']
                log(f"ENTRY: {e} [{ALGO_VERSION}]")
                # Cache entry fields for CH trade_events on exit
                tid = self._resolve_trade_id(e.get('trade_id'), create_if_missing=True)
                e['trade_id'] = tid
                if tid:
                    efsm_decision = None
                    overlay_flip = False
                    if self._efsm is not None and int(e.get('direction', -1)) == 1 and int(self.trade_direction) == -1:
                        efsm_decision = self._efsm.tag_next_entry(
                            asset=str(e.get('asset', '') or ''),
                            entry_ts=datetime.now(timezone.utc),
                            metadata={"trade_id": tid},
                        )
                        overlay_flip = bool(efsm_decision and efsm_decision.action == "TAG" and efsm_decision.side == "LONG")
                    self._pending_entries[tid] = {
                        'trade_id':     tid,
                        'asset':          e.get('asset', ''),
                        'side':           'SHORT' if e.get('direction', -1) == -1 else 'LONG',
                        'entry_price':    float(e.get('entry_price', 0) or 0),
                        'quantity':       round(float(e.get('notional', 0) or 0) / float(e.get('entry_price', 1) or 1), 6),
                        'notional':       float(e.get('notional', 0) or 0),
                        'notional_entry': float(e.get('notional', 0) or 0),
                        'leverage':       float(e.get('leverage', 0) or 0),
                        'vel_div_entry':  float(e.get('vel_div', 0) or 0),
                        'boost_at_entry': float(getattr(getattr(self, 'eng', None), 'acb_boost', 1.0) or 1.0),
                        'beta_at_entry':  float(getattr(getattr(self, 'eng', None), 'acb_beta',  1.0) or 1.0),
                        'posture':        posture_now,
                        'entry_ts':       _ch_ts_us(),
                        'entry_date':     (self.current_day or ''),
                        'entry_bar':      self.bar_idx,
                        'overlay_flip':   overlay_flip,
                        'overlay_reason': getattr(efsm_decision, "reason", "") if efsm_decision else "",
                        'overlay_slot':   int(getattr(efsm_decision, "consumed_slot", 0) or 0) if efsm_decision else 0,
                        'retraction_legs': 0,
                        'realized_pnl_legs_total': 0.0,
                    }
                    self._pending_entries[tid].update(self._chain_state_for_pending(
                        tid,
                        self._pending_entries[tid],
                        chain_mode="LIVE",
                        chain_head_leg_id=f"{tid}:open",
                        chain_prev_leg_id="",
                        chain_seq=0,
                    ))
                    if overlay_flip:
                        log(
                            f"EFSM TAG: trade_id={tid} asset={e.get('asset','')} "
                            f"slot={self._pending_entries[tid]['overlay_slot']} "
                            f"reason={self._pending_entries[tid]['overlay_reason']}"
                        )
                    # Persist position to CH so restarts can recover it
                    self._ps_write_open(tid, self._pending_entries[tid])
                    ch_put("trade_reconstruction", {
                        "ts": _ch_ts_us(),
                        "trade_id": tid,
                        "event_type": "OPEN",
                        "event_id": f"{tid}:open",
                        "payload_json": json.dumps(self._pending_entries[tid], default=str),
                    })
                    self._announce_position_event(
                        kind="trade_entry",
                        severity="info",
                        title=f"[BLUE] ENTRY {e.get('asset', '')} {self._pending_entries[tid]['side']}",
                        message=(
                            f"entry={float(e.get('entry_price', 0) or 0):.6f} "
                            f"qty={self._pending_entries[tid]['quantity']:.6f} "
                            f"lev={self._pending_entries[tid]['leverage']:.2f}x"
                        ),
                        metadata={
                            "trade_id": tid,
                            "asset": self._pending_entries[tid]["asset"],
                            "side": self._pending_entries[tid]["side"],
                            "entry_price": self._pending_entries[tid]["entry_price"],
                            "quantity": self._pending_entries[tid]["quantity"],
                            "leverage": self._pending_entries[tid]["leverage"],
                            "vel_div_entry": self._pending_entries[tid]["vel_div_entry"],
                            "boost_at_entry": self._pending_entries[tid]["boost_at_entry"],
                            "beta_at_entry": self._pending_entries[tid]["beta_at_entry"],
                            "posture": self._pending_entries[tid]["posture"],
                            "entry_ts": self._pending_entries[tid]["entry_ts"],
                        },
                    )
                    if self._v7_exit_engine is not None:
                        try:
                            side = 1 if e.get('direction', -1) == -1 else 0
                            ctx = self._v7_exit_engine.make_context(
                                entry_price=float(e.get('entry_price', 0) or 0),
                                entry_bar=max(0, self.bar_idx - 1),
                                side=side,
                            )
                            if self._last_exf:
                                ctx.set_exf(
                                    funding=float(self._last_exf.get('funding', 0.0) or 0.0),
                                    dvol=float(self._last_exf.get('dvol', 0.0) or 0.0),
                                    fear_greed=float(self._last_exf.get('fear_greed', 0.0) or 0.0),
                                    taker=float(self._last_exf.get('taker', 0.0) or 0.0),
                                )
                            self._v7_contexts[tid] = ctx
                            self._v7_decisions.pop(tid, None)
                            self._v7_decision_seq[tid] = 0
                        except Exception as e:
                            log(f"  V7 live context init failed for {tid}: {e}")
                    # Shadow AE: notify of entry (vel_div at entry bar is in scope)
                    if self._ae is not None:
                        try:
                            self._ae.on_entry(
                                trade_id=tid,
                                asset=e.get('asset', ''),
                                direction=int(e.get('direction', -1)),
                                entry_price=float(e.get('entry_price', 0) or 0),
                                vel_div_entry=vel_div,
                            )
                        except Exception:
                            pass
                    if self._sc_advisor is not None:
                        try:
                            payload = self._read_esof_payload()
                            rec = self._sc_advisor.evaluate(
                                trade_id=tid,
                                asset=e.get('asset', ''),
                                sc=_safe_float(payload.get('advisory_score', payload.get('score', 0.0)) if payload else None),
                                vel_div=vel_div,
                                exf_snapshot=getattr(self, "_last_exf", {}) or {},
                                trade_history=getattr(self.eng, 'trade_history', []),
                                current_mult=float(self._last_esof_size_mult or 1.0),
                                esof_payload=payload,
                                scan_number=scan_number,
                                bar_idx=self.bar_idx,
                                strategy="blue",
                                log_shadow=True,
                            )
                            self._pending_entries[tid]['sc_threshold_advisor'] = rec
                            self._pending_entries[tid]['sc_exec_mult'] = float(self._last_esof_size_mult or 1.0)
                        except Exception:
                            pass
                    if self._sc_gauge is not None:
                        try:
                            payload = self._read_esof_payload()
                            rec = self._sc_gauge.evaluate(
                                trade_id=tid,
                                asset=e.get('asset', ''),
                                sc=_safe_float(payload.get('advisory_score', payload.get('score', 0.0)) if payload else None),
                                vel_div=vel_div,
                                exf_snapshot=getattr(self, "_last_exf", {}) or {},
                                obf_snapshot=self._current_obf_snapshot(e.get('asset', ''), self.bar_idx),
                                trade_history=getattr(self.eng, 'trade_history', []),
                                current_mult=float(self._last_esof_size_mult or 1.0),
                                esof_payload=payload,
                                scan_number=scan_number,
                                bar_idx=self.bar_idx,
                                strategy="blue",
                                log_shadow=True,
                            )
                            self._pending_entries[tid]['sc_bucket_gauge'] = rec
                            self._pending_entries[tid]['sc_bucket_gauge_exec_mult'] = float(self._last_esof_size_mult or 1.0)
                        except Exception:
                            pass
                    if self._bounce_advisor is not None:
                        try:
                            entry_ts_val = float(self._pending_entries[tid].get('entry_ts', 0) or 0)
                            entry_ts_dt = datetime.fromtimestamp(entry_ts_val / 1_000_000, tz=timezone.utc) if entry_ts_val else None
                            bounce_rec = self._bounce_eval(
                                trade_id=tid,
                                asset=str(e.get('asset', '')),
                                side=self._pending_entries[tid]['side'],
                                source="entry",
                                scan_number=scan_number,
                                entry_ts=entry_ts_dt,
                                current_price=float(prices_dict.get(e.get('asset', ''), e.get('entry_price', 0)) or e.get('entry_price', 0) or 0),
                                entry_price=float(e.get('entry_price', 0) or 0),
                                quantity=float(self._pending_entries[tid].get('quantity', 0) or 0),
                                notional=float(e.get('notional', 0) or 0),
                                leverage=float(e.get('leverage', 0) or 0),
                                vel_div=vel_div,
                                current_mult=float(self._last_esof_size_mult or 1.0),
                                bars_held=0,
                                log_shadow=True,
                            )
                            if bounce_rec:
                                self._pending_entries[tid]['bounce_advisor_entry'] = bounce_rec
                                self._pending_entries[tid]['bounce_advisor_latest'] = bounce_rec
                        except Exception as e:
                            log(f"  BounceAdvisor entry eval failed for {tid}: {e}")

            if (self._v7_exit_engine is not None
                    and self.eng is not None
                    and getattr(self.eng, 'position', None) is not None
                    and not self._v7_live_exit_enabled):
                pos = self.eng.position
                tid_v7 = getattr(pos, 'trade_id', '')
                pending_v7 = self._pending_entries.get(tid_v7, {})
                ctx_v7 = self._v7_contexts.get(tid_v7)
                if ctx_v7 is None and pending_v7:
                    try:
                        ctx_v7 = self._v7_exit_engine.make_context(
                            entry_price=float(pending_v7.get('entry_price', pos.entry_price) or pos.entry_price or 0.0),
                            entry_bar=int(pending_v7.get('entry_bar', max(0, self.bar_idx - 1)) or max(0, self.bar_idx - 1)),
                            side=1 if pending_v7.get('side', 'SHORT') == 'SHORT' else 0,
                        )
                        if self._last_exf:
                            ctx_v7.set_exf(
                                funding=float(self._last_exf.get('funding', 0.0) or 0.0),
                                dvol=float(self._last_exf.get('dvol', 0.0) or 0.0),
                                fear_greed=float(self._last_exf.get('fear_greed', 0.0) or 0.0),
                                taker=float(self._last_exf.get('taker', 0.0) or 0.0),
                            )
                        self._v7_contexts[tid_v7] = ctx_v7
                        self._v7_decision_seq.setdefault(tid_v7, 0)
                    except Exception as e:
                        log(f"  V7 live context restore failed for {tid_v7}: {e}")
                        ctx_v7 = None
                if ctx_v7 is not None and pending_v7:
                    try:
                        if self.ob_eng is not None:
                            ob_sig = self.ob_eng.get_signal(pos.asset, float(max(0, self.bar_idx - 1)))
                            ob_imb = float(getattr(ob_sig, 'imbalance_ma5', 0.0) or 0.0)
                        else:
                            ob_imb = 0.0
                        cur_px = float(prices_dict.get(pos.asset, pos.current_price) or pos.current_price or 0.0)
                        if cur_px > 0.0:
                            v7dec = self._v7_exit_engine.evaluate(
                                ctx_v7,
                                cur_px,
                                max(0, self.bar_idx - 1),
                                ob_imb,
                                asset=pos.asset,
                            )
                            self._v7_decisions[tid_v7] = v7dec
                            self._record_v7_decision(
                                trade_id=tid_v7,
                                asset=pos.asset,
                                side=pending_v7.get('side', 'SHORT'),
                                decision=v7dec,
                                current_price=cur_px,
                                ob_imbalance=ob_imb,
                                vel_div_now=vel_div,
                                v50_vel=v50_vel,
                                v750_vel=v750_vel,
                                bar_idx=max(0, self.bar_idx - 1),
                            )
                            v7_action = str(v7dec.get("action", "") if isinstance(v7dec, dict) else getattr(v7dec, "action", "")).upper()
                            if v7_action == "RETRACT":
                                try:
                                    cmd = {
                                        "command_id": f"v7-retract-{uuid.uuid4().hex[:16]}",
                                        "trade_id": tid_v7,
                                        "action": "RETRACT",
                                        "fraction": 0.50,
                                        "reason": "V7_RETRACT",
                                        "source": "v7",
                                        "ts": float(time.time()),
                                        "asset": pos.asset,
                                        "chain_root_trade_id": str(pending_v7.get("chain_root_trade_id", tid_v7) or tid_v7),
                                        "chain_head_leg_id": str(pending_v7.get("chain_head_leg_id", f"{tid_v7}:open") or f"{tid_v7}:open"),
                                        "chain_prev_leg_id": str(pending_v7.get("chain_prev_leg_id", "") or ""),
                                        "chain_seq": int(pending_v7.get("chain_seq", pending_v7.get("retraction_legs", 0)) or 0),
                                        "chain_token": str(pending_v7.get("chain_token", "") or ""),
                                    }
                                    raw_q = self.control_map.blocking().get("blue_runtime_commands") if self.control_map else None
                                    q = json.loads(raw_q) if isinstance(raw_q, str) and raw_q else []
                                    if not isinstance(q, list):
                                        q = []
                                    q.append(cmd)
                                    q = q[-200:]
                                    if self.control_map is not None:
                                        self.control_map.blocking().put("blue_runtime_commands", json.dumps(q))
                                except Exception as e:
                                    log(f"  V7 retract enqueue failed for {tid_v7}: {e}")
                        if self._bounce_advisor is not None:
                            try:
                                entry_ts_val = float(pending_v7.get('entry_ts', 0) or 0)
                                entry_ts_dt = datetime.fromtimestamp(entry_ts_val / 1_000_000, tz=timezone.utc) if entry_ts_val else None
                                bounce_rec = self._bounce_eval(
                                    trade_id=tid_v7,
                                    asset=pos.asset,
                                    side=pending_v7.get('side', 'SHORT'),
                                    source="open_scan",
                                    scan_number=scan_number,
                                    entry_ts=entry_ts_dt,
                                    current_price=cur_px,
                                    entry_price=float(pending_v7.get('entry_price', pos.entry_price) or pos.entry_price or 0.0),
                                    quantity=float(pending_v7.get('quantity', getattr(pos, 'quantity', 0.0)) or getattr(pos, 'quantity', 0.0) or 0.0),
                                    notional=float(pending_v7.get('notional', getattr(pos, 'notional', 0.0)) or getattr(pos, 'notional', 0.0) or 0.0),
                                    leverage=float(pending_v7.get('leverage', getattr(pos, 'leverage', 0.0)) or getattr(pos, 'leverage', 0.0) or 0.0),
                                    vel_div=vel_div,
                                    current_mult=float(self._last_esof_size_mult or 1.0),
                                    bars_held=max(0, int(self.bar_idx - int(pending_v7.get('entry_bar', max(0, self.bar_idx - 1)) or max(0, self.bar_idx - 1)))),
                                    log_shadow=True,
                                )
                                if bounce_rec:
                                    pending_v7['bounce_advisor_latest'] = bounce_rec
                                    self._pending_entries[tid_v7] = pending_v7
                            except Exception as e:
                                log(f"  BounceAdvisor open-scan eval failed for {tid_v7}: {e}")
                    except Exception as e:
                        log(f"  V7 live evaluate failed for {tid_v7}: {e}")

            _forced_exit = self._process_runtime_commands(prices_dict)
            if _forced_exit is not None and not result.get('exit'):
                result['exit'] = _forced_exit

            if result.get('exit'):
                x = result['exit']
                tid = x.get('trade_id')
                # Hibernate-protected exits: re-label reason, finalize posture
                if tid and self._hibernate_protect_active == tid:
                    _orig = x.get('reason', '')
                    _map = {'FIXED_TP': 'HIBERNATE_TP', 'STOP_LOSS': 'HIBERNATE_SL',
                            'MAX_HOLD': 'HIBERNATE_MAXHOLD'}
                    x['reason'] = _map.get(_orig, f'HIBERNATE_{_orig}')
                    self._hibernate_protect_active = None
                    # Position closed — now safe to commit posture to HIBERNATE
                    _cur_posture = self._read_posture()
                    if _cur_posture == 'HIBERNATE':
                        self.eng._day_posture = 'HIBERNATE'
                        log(f"HIBERNATE_PROTECT: closed via {x['reason']} — posture finalized HIBERNATE")
                    else:
                        log(f"HIBERNATE_PROTECT: closed via {x['reason']} — posture recovered to {_cur_posture}")
                x['reason'] = _normalize_v7_exit_reason(x.get('reason', ''))
                log(f"EXIT: {x} [{ALGO_VERSION}]")
                _exit_reason_raw = str(x.get('reason', ''))
                if _exit_reason_raw in ('FIXED_TP', 'HIBERNATE_TP'):
                    _tp_used = self.eng.exit_manager.fixed_tp_pct
                    _pos = self.eng.position
                    _bars = int(x.get('bars_held', 0) or 0)
                    log(f"  TP_EXIT: tp_pct={_tp_used*100:.2f}% bars_held={_bars} "
                        f"pnl_pct={float(x.get('pnl_pct',0) or 0):+.4f}")
                tid = self._resolve_trade_id(x.get('trade_id'), create_if_missing=True)
                x['trade_id'] = tid
                pending = self._pending_entries.pop(tid, {}) if tid else {}
                if tid:
                    self._v7_contexts.pop(tid, None)
                    self._v7_decisions.pop(tid, None)
                    self._v7_decision_seq.pop(tid, None)
                if pending:
                    # exact bar price the engine exited against — prices_dict is still in scope
                    exit_price = float(prices_dict.get(pending['asset'], 0) or 0)
                    if self._sc_advisor is not None:
                        try:
                            _rec = pending.get('sc_threshold_advisor')
                            if _rec:
                                self._sc_advisor.observe_outcome(
                                    _rec,
                                    executed_mult=float(pending.get('sc_exec_mult', self._last_esof_size_mult) or 1.0),
                                    pnl_pct=float(x.get('pnl_pct', 0) or 0),
                                    exit_reason=str(x.get('reason', 'UNKNOWN')),
                                )
                        except Exception:
                            pass
                    if self._sc_gauge is not None:
                        try:
                            _rec = pending.get('sc_bucket_gauge')
                            if _rec:
                                self._sc_gauge.observe_outcome(
                                    _rec,
                                    executed_mult=float(pending.get('sc_bucket_gauge_exec_mult', self._last_esof_size_mult) or 1.0),
                                    pnl_pct=float(x.get('pnl_pct', 0) or 0),
                                    exit_reason=str(x.get('reason', 'UNKNOWN')),
                                )
                        except Exception:
                            pass
                    if self._bounce_advisor is not None:
                        try:
                            _bounce_rec = pending.get('bounce_advisor_entry')
                            if _bounce_rec:
                                self._bounce_advisor.observe_outcome(
                                    _bounce_rec,
                                    pnl_pct=float(x.get('pnl_pct', 0) or 0),
                                    exit_reason=str(x.get('reason', 'UNKNOWN')),
                                )
                        except Exception as e:
                            log(f"  BounceAdvisor outcome update failed for {tid}: {e}")
                    if self._market_state_runtime is not None:
                        try:
                            self._market_state_runtime.online_update_from_trade(
                                asset=str(pending.get("asset", "")),
                                entry_price=float(pending.get("entry_price", 0) or 0),
                                exit_price=float(exit_price),
                                direction=-1 if str(pending.get("side", "SHORT")).upper() == "SHORT" else 1,
                                pnl_pct=float(x.get("pnl_pct", 0) or 0),
                                bars_held=int(x.get("bars_held", 0) or 0),
                                exit_reason=str(x.get("reason", "UNKNOWN")),
                                trade_id=str(tid or ""),
                                leverage=float(pending.get("leverage", 1.0) or 1.0),
                            )
                        except Exception as e:
                            log(f"  MarketStateRuntime outcome update failed for {tid}: {e}")
                    if self._efsm is not None:
                        try:
                            _efsm_out = self._efsm.observe_closed_trade(
                                trade_id=str(tid or ""),
                                asset=str(pending.get("asset", "") or ""),
                                side=str(pending.get("side", "SHORT") or "SHORT"),
                                pnl=float(x.get("net_pnl", 0) or 0),
                                pnl_pct=float(x.get("pnl_pct", 0) or 0),
                                leverage=float(pending.get("leverage", 0) or 0),
                                closed_ts=datetime.now(timezone.utc),
                                was_overlay_flip=bool(pending.get("overlay_flip", False)),
                                metadata={"exit_reason": str(x.get("reason", "UNKNOWN"))},
                            )
                            if _efsm_out.action in {"ARMED", "TAG", "RESET"}:
                                log(f"EFSM { _efsm_out.action }: { _efsm_out.to_dict() }")
                        except Exception as e:
                            log(f"  EFSM observe_closed_trade failed for {tid}: {e}")
                    ch_put("trade_events", {
                        "ts":             _ch_ts_us(),
                        "date":           pending['entry_date'],
                        "strategy":       "blue",
                        "trade_id":       tid,
                        "asset":          pending['asset'],
                        "side":           pending['side'],
                        "entry_price":    pending['entry_price'],
                        "exit_price":     exit_price,
                        "quantity":       pending['quantity'],
                        "pnl":            float(x.get('net_pnl', 0) or 0),
                        "pnl_pct":        float(x.get('pnl_pct', 0) or 0),
                        "exit_reason":    str(x.get('reason', 'UNKNOWN')),
                        "vel_div_entry":  pending['vel_div_entry'],
                        "boost_at_entry": pending['boost_at_entry'],
                        "beta_at_entry":  pending['beta_at_entry'],
                        "posture":        pending['posture'],
                        "leverage":       pending['leverage'],
                        "bars_held":      int(x.get('bars_held', 0) or 0),
                        "regime_signal":  0,
                        "tp_threshold":   float(self.eng.exit_manager.fixed_tp_pct),
                    })
                    ch_put("trade_reconstruction", {
                        "ts": _ch_ts_us(),
                        "trade_id": str(tid or ""),
                        "event_type": "CLOSE",
                        "event_id": f"{tid}:close",
                        "payload_json": json.dumps({
                            "exit": x,
                            "pending": pending,
                            "exit_price": exit_price,
                            "retraction_legs": int(pending.get("retraction_legs", 0) or 0),
                            "retraction_realized_total": float(pending.get("realized_pnl_legs_total", 0.0) or 0.0),
                            "chain": {
                                "trade_id": tid,
                                "chain_root_trade_id": pending.get("chain_root_trade_id", tid),
                                "chain_head_leg_id": pending.get("chain_head_leg_id", f"{tid}:open"),
                                "chain_prev_leg_id": pending.get("chain_prev_leg_id", ""),
                                "chain_seq": int(pending.get("retraction_legs", 0) or 0),
                                "chain_token": pending.get("chain_token", ""),
                                "chain_mode": pending.get("chain_mode", "LIVE"),
                            },
                        }, default=str),
                    })
                    # Mark position closed in CH (supersedes OPEN row via ReplacingMergeTree)
                    self._ps_write_closed(tid, pending, x)
                    self._announce_position_event(
                        kind="trade_exit",
                        severity="info" if float(x.get("pnl_pct", 0) or 0) >= 0 else "warning",
                        title=f"[BLUE] EXIT {pending.get('asset', '')} {pending.get('side', '')}",
                        message=(
                            f"reason={x.get('reason', 'UNKNOWN')} "
                            f"pnl={float(x.get('net_pnl', 0) or 0):+.2f} "
                            f"pnl_pct={float(x.get('pnl_pct', 0) or 0):+.3%}"
                        ),
                        metadata={
                            "trade_id": tid,
                            "asset": pending.get("asset", ""),
                            "side": pending.get("side", ""),
                            "entry_price": pending.get("entry_price", 0),
                            "exit_price": exit_price,
                            "quantity": pending.get("quantity", 0),
                            "pnl": float(x.get("net_pnl", 0) or 0),
                            "pnl_pct": float(x.get("pnl_pct", 0) or 0),
                            "exit_reason": str(x.get("reason", "UNKNOWN")),
                            "bars_held": int(x.get("bars_held", 0) or 0),
                            "posture": pending.get("posture", ""),
                            "overlay_flip": bool(pending.get("overlay_flip", False)),
                            "overlay_reason": str(pending.get("overlay_reason", "")),
                            "overlay_slot": int(pending.get("overlay_slot", 0) or 0),
                        },
                    )
                    # Shadow AE: record outcome for online update
                    if self._ae is not None and tid:
                        try:
                            self._ae.on_exit(
                                trade_id=tid,
                                actual_exit_reason=str(x.get('reason', 'UNKNOWN')),
                                pnl_pct=float(x.get('pnl_pct', 0) or 0),
                            )
                        except Exception:
                            pass

            # Shadow AE: per-bar evaluate for all open trades — daemon thread, zero hot-path impact
            if self._ae is not None and self._pending_entries:
                _ae_ref = self._ae
                _pending_snap = dict(self._pending_entries)  # shallow copy under GIL
                _prices_snap  = dict(prices_dict)
                _vel_now      = vel_div
                _bar          = self.bar_idx
                def _ae_eval():
                    for _tid, _p in _pending_snap.items():
                        try:
                            _cur = _prices_snap.get(_p['asset'], 0) or 0
                            if not _cur:
                                continue
                            _entry_px = float(_p.get('entry_price', 0) or 0)
                            _bars_held = max(0, int(_bar - int(_p.get('entry_bar', _bar))))
                            _shadow_pnl_pct = ((_entry_px - _cur) / _entry_px) if _entry_px > 0 else 0.0
                            _recent_prices = self._bounce_price_path(_p['asset'])
                            _shadow = _ae_ref.evaluate(
                                trade_id=_tid,
                                asset=_p['asset'],
                                direction=-1,
                                entry_price=_entry_px,
                                current_price=_cur,
                                bars_held=_bars_held,
                                vel_div_now=_vel_now,
                            )
                            _ae_ref.log_shadow(_shadow, pnl_pct=_shadow_pnl_pct)
                            if self._advanced_sl is not None:
                                try:
                                    _ms_state = dict(self._market_state_runtime.latest_state) if self._market_state_runtime and getattr(self._market_state_runtime, "latest_state", None) else {}
                                    _ms_bundle = dict(self._market_state_runtime.latest_bundle_dict) if self._market_state_runtime and getattr(self._market_state_runtime, "latest_bundle_dict", None) else {}
                                    _v7 = dict(self._v7_decisions.get(_tid, {}) or {})
                                    _adv = self._advanced_sl.evaluate(
                                        trade_id=_tid,
                                        asset=_p['asset'],
                                        side=str(_p.get("side", "SHORT") or "SHORT"),
                                        entry_price=_entry_px,
                                        current_price=_cur,
                                        bars_held=_bars_held,
                                        recent_prices=_recent_prices,
                                        ae_shadow=_shadow,
                                        v7_decision=_v7,
                                        market_state=_ms_state,
                                        market_bundle=_ms_bundle,
                                        exf_snapshot=dict(self._last_exf or {}),
                                    )
                                    self._advanced_sl.log_shadow(_adv, pnl_pct=_shadow_pnl_pct)
                                except Exception:
                                    pass
                        except Exception:
                            pass
                threading.Thread(target=_ae_eval, daemon=True).start()

            self._push_state(scan_number, vel_div, vol_ok, self._read_posture())
            
        except Exception as e:
            log(f"ERROR in _process_scan: {e}")

    def on_exf_update(self, event):
        if not event.value: return
        snapshot = json.loads(event.value) if isinstance(event.value, str) else event.value
        if not self.current_day or not self.acb: return
        try:
            self._last_exf = {
                'funding':    float(snapshot.get('funding_btc', 0.0)),
                'dvol':       float(snapshot.get('dvol_btc', 50.0)),
                'fear_greed': float(snapshot.get('fng', 50.0)),
                'taker':      float(snapshot.get('taker', 0.5)),
            }
            w750_vel = getattr(self, 'last_w750_vel', None)
            acb_info = self.acb.get_dynamic_boost_from_hz(
                date_str=self.current_day,
                exf_snapshot=snapshot,
                w750_velocity=float(w750_vel) if w750_vel else None,
                direction=self.trade_direction,
            )
            with self.eng_lock:
                if hasattr(self.eng, 'update_acb_boost'):
                    subday_exit = self.eng.update_acb_boost(
                        boost=acb_info['boost'],
                        beta=acb_info['beta']
                    )
                    if subday_exit is not None:
                        log(f"SUBDAY_EXIT: {subday_exit} [{ALGO_VERSION}]")
                        tid = self._resolve_trade_id(subday_exit.get('trade_id'), create_if_missing=True)
                        subday_exit['trade_id'] = tid
                        pending = {}
                        if tid:
                            pending = self._pending_entries.pop(tid, {})
                        if pending and self._sc_advisor is not None:
                            try:
                                _rec = pending.get('sc_threshold_advisor')
                                if _rec:
                                    self._sc_advisor.observe_outcome(
                                        _rec,
                                        executed_mult=float(pending.get('sc_exec_mult', self._last_esof_size_mult) or 1.0),
                                        pnl_pct=float(subday_exit.get('pnl_pct', 0) or 0),
                                        exit_reason=str(subday_exit.get('reason', 'SUBDAY_ACB_NORMALIZATION')),
                                    )
                            except Exception:
                                pass
                        if pending and self._sc_gauge is not None:
                            try:
                                _rec_g = pending.get('sc_bucket_gauge')
                                if _rec_g:
                                    self._sc_gauge.observe_outcome(
                                        _rec_g,
                                        executed_mult=float(pending.get('sc_bucket_gauge_exec_mult', self._last_esof_size_mult) or 1.0),
                                        pnl_pct=float(subday_exit.get('pnl_pct', 0) or 0),
                                        exit_reason=str(subday_exit.get('reason', 'SUBDAY_ACB_NORMALIZATION')),
                                    )
                            except Exception:
                                pass
                        if pending and self._bounce_advisor is not None:
                            try:
                                _bounce_rec = pending.get('bounce_advisor_entry')
                                if _bounce_rec:
                                    self._bounce_advisor.observe_outcome(
                                        _bounce_rec,
                                        pnl_pct=float(subday_exit.get('pnl_pct', 0) or 0),
                                        exit_reason=str(subday_exit.get('reason', 'SUBDAY_ACB_NORMALIZATION')),
                                    )
                            except Exception as e:
                                log(f"  BounceAdvisor outcome update failed for {tid}: {e}")
                        if self._market_state_runtime is not None:
                            try:
                                self._market_state_runtime.online_update_from_trade(
                                    asset=str(pending.get("asset", "")),
                                    entry_price=float(pending.get("entry_price", 0) or 0),
                                    exit_price=float(subday_exit.get("exit_price", 0) or 0),
                                    direction=-1 if str(pending.get("side", "SHORT")).upper() == "SHORT" else 1,
                                    pnl_pct=float(subday_exit.get("pnl_pct", 0) or 0),
                                    bars_held=int(subday_exit.get("bars_held", 0) or 0),
                                    exit_reason=str(subday_exit.get("reason", "SUBDAY_ACB_NORMALIZATION")),
                                    trade_id=str(tid or ""),
                                    leverage=float(pending.get("leverage", 1.0) or 1.0),
                                )
                            except Exception as e:
                                log(f"  MarketStateRuntime outcome update failed for {tid}: {e}")
                        if self._efsm is not None:
                            try:
                                _efsm_sub = self._efsm.observe_closed_trade(
                                    trade_id=str(tid or ""),
                                    asset=str(pending.get("asset", "") or ""),
                                    side=str(pending.get("side", "SHORT") or "SHORT"),
                                    pnl=float(subday_exit.get("net_pnl", 0) or 0),
                                    pnl_pct=float(subday_exit.get("pnl_pct", 0) or 0),
                                    leverage=float(pending.get("leverage", 0) or 0),
                                    closed_ts=datetime.now(timezone.utc),
                                    was_overlay_flip=bool(pending.get("overlay_flip", False)),
                                    metadata={"exit_reason": str(subday_exit.get("reason", "SUBDAY_ACB_NORMALIZATION"))},
                                )
                                if _efsm_sub.action in {"ARMED", "TAG", "RESET"}:
                                    log(f"EFSM { _efsm_sub.action }: { _efsm_sub.to_dict() }")
                            except Exception as e:
                                log(f"  EFSM observe_closed_trade failed for {tid}: {e}")
                        ch_put("trade_events", {
                            "ts":             _ch_ts_us(),
                            "date":           self.current_day or '',
                            "strategy":       "blue",
                            "trade_id":       tid,
                            "asset":          pending.get('asset', subday_exit.get('asset', '')),
                            "side":           pending.get('side', 'SHORT'),
                            "entry_price":    pending.get('entry_price', 0),
                            "exit_price":     float(subday_exit.get('exit_price', 0) or 0),
                            "quantity":       round(float(pending.get('notional', 0) or 0) / max(float(pending.get('entry_price', 1) or 1), 1e-12), 6),
                            "pnl":            float(subday_exit.get('net_pnl', 0) or 0),
                            "pnl_pct":        float(subday_exit.get('pnl_pct', 0) or 0),
                            "exit_reason":    str(subday_exit.get('reason', 'SUBDAY_ACB_NORMALIZATION')),
                            "vel_div_entry":  float(pending.get('vel_div_entry', 0) or 0),
                            "boost_at_entry": float(pending.get('boost_at_entry', 0) or 0),
                            "beta_at_entry":  float(pending.get('beta_at_entry', 0) or 0),
                            "posture":        pending.get('posture', ''),
                            "leverage":       float(pending.get('leverage', 0) or 0),
                            "bars_held":      int(subday_exit.get('bars_held', 0) or 0),
                            "regime_signal":  0,
                        })
                        self._announce_position_event(
                            kind="trade_exit",
                            severity="info" if float(subday_exit.get("pnl_pct", 0) or 0) >= 0 else "warning",
                            title=f"[BLUE] EXIT {pending.get('asset', '')} {pending.get('side', '')}",
                            message=(
                                f"reason={subday_exit.get('reason', 'SUBDAY_ACB_NORMALIZATION')} "
                                f"pnl={float(subday_exit.get('net_pnl', 0) or 0):+.2f} "
                                f"pnl_pct={float(subday_exit.get('pnl_pct', 0) or 0):+.3%}"
                            ),
                            metadata={
                            "trade_id": tid,
                            "asset": pending.get("asset", subday_exit.get("asset", "")),
                                "side": pending.get("side", "SHORT"),
                                "entry_price": pending.get("entry_price", 0),
                                "exit_price": float(subday_exit.get("exit_price", 0) or 0),
                                "quantity": round(float(pending.get("notional", 0) or 0) / max(float(pending.get("entry_price", 1) or 1), 1e-12), 6),
                                "pnl": float(subday_exit.get("net_pnl", 0) or 0),
                                "pnl_pct": float(subday_exit.get("pnl_pct", 0) or 0),
                                "exit_reason": str(subday_exit.get("reason", "SUBDAY_ACB_NORMALIZATION")),
                                "bars_held": int(subday_exit.get("bars_held", 0) or 0),
                                "posture": pending.get("posture", ""),
                                "overlay_flip": bool(pending.get("overlay_flip", False)),
                                "overlay_reason": str(pending.get("overlay_reason", "")),
                                "overlay_slot": int(pending.get("overlay_slot", 0) or 0),
                            },
                        )
            now = time.time()
            if now - self._exf_log_time >= 300:
                self._exf_log_time = now
                log(f"ACB subday: boost={acb_info['boost']:.4f} beta={acb_info['beta']:.4f} "
                    f"signals={acb_info['signals']:.1f} src={acb_info.get('source','?')}")
            # ACB_EXIT disabled: update_acb_boost() called to keep boost/beta current
            # (ACBv6 intact), but SUBDAY_ACB_NORMALIZATION exits are suppressed.
        except ValueError as e:
            log(f"ACB Stale Data Fallback: {e}")
        except Exception as e:
            log(f"on_exf_update Error: {e}")
            
    def _wire_obf(self, assets):
        if not assets or self.ob_assets:
            return
        self.ob_assets = assets
        from nautilus_dolphin.nautilus.hz_ob_provider import HZOBProvider
        live_ob = HZOBProvider(
            hz_cluster=HZ_CLUSTER,
            hz_host=HZ_HOST,
            assets=assets,
        )
        self.ob_eng = OBFeatureEngine(live_ob)
        # No preload_date() call — live mode uses step_live() per scan
        self.eng.set_ob_engine(self.ob_eng)
        log(f"  OBF wired: HZOBProvider, {len(assets)} assets (LIVE mode)")
        
    def _save_capital(self):
        """Persist capital to HZ (primary) and disk (fallback) so restarts survive HZ loss."""
        capital = getattr(self.eng, 'capital', None)
        if capital is None or not math.isfinite(capital) or capital < 1.0:
            return
        payload = json.dumps({'capital': capital, 'ts': time.time()})
        # Primary: Hazelcast
        try:
            self.state_map.blocking().put('capital_checkpoint', payload)
        except Exception as e:
            log(f"  capital HZ save failed: {e}")
        # Secondary: local disk (survives HZ restart)
        try:
            CAPITAL_DISK_CHECKPOINT.write_text(payload)
        except Exception as e:
            log(f"  capital disk save failed: {e}")

    def _restore_capital(self):
        """Restore capital from live HZ state or ledger-backed snapshots.

        The raw scalar checkpoint is legacy-only and requires the explicit
        DOLPHIN_ALLOW_LEGACY_CAPITAL_CHECKPOINT=1 escape hatch.
        """
        self._restore_failed = False
        self._restore_failure_reason = ""
        self._restore_source = ""
        if self._restore_capital_from_state():
            return
        log("  Capital: no sane state source found — restore halted")

    def _push_state(self, scan_number, vel_div, vol_ok, posture):
        try:
            with self.eng_lock:
                capital = getattr(self.eng, 'capital', 25000.0)
                # Engine uses a single NDPosition object, not a list
                pos = getattr(self.eng, 'position', None)
                if pos is not None:
                    pending = self._pending_entries.get(getattr(pos, "trade_id", ""), {})
                    open_notional = float(getattr(pos, 'notional', 0) or 0)
                    open_positions_list = [{
                        'trade_id':     getattr(pos, 'trade_id', ''),
                        'asset':        pos.asset,
                        'side':         'SHORT' if pos.direction == -1 else 'LONG',
                        'entry_price':  pos.entry_price,
                        'quantity':     round(open_notional / pos.entry_price, 6) if pos.entry_price else 0,
                        'notional':     open_notional,
                        'retraction_legs': int(pending.get('retraction_legs', 0) or 0),
                        'realized_pnl_legs_total': float(pending.get('realized_pnl_legs_total', 0.0) or 0.0),
                        'chain_root_trade_id': str(pending.get('chain_root_trade_id', getattr(pos, 'trade_id', '')) or getattr(pos, 'trade_id', '')),
                        'chain_head_leg_id': str(pending.get('chain_head_leg_id', f"{getattr(pos, 'trade_id', '')}:open") or f"{getattr(pos, 'trade_id', '')}:open"),
                        'chain_prev_leg_id': str(pending.get('chain_prev_leg_id', '') or ''),
                        'chain_seq': int(pending.get('chain_seq', pending.get('retraction_legs', 0)) or 0),
                        'chain_token': str(pending.get('chain_token', '') or ''),
                        'leverage':     float(getattr(pos, 'leverage', 0) or 0),
                        'unrealized_pnl': round(pos.pnl_pct * open_notional, 2),
                    }]
                else:
                    open_notional = 0.0
                    open_positions_list = []
                cur_leverage = (open_notional / capital) if capital and capital > 0 and math.isfinite(capital) else 0.0

                snapshot = {
                    'capital': capital if math.isfinite(capital) else None,
                    'open_positions': open_positions_list,
                    'algo_version': ALGO_VERSION,
                    'last_scan_number': scan_number, 'last_vel_div': vel_div,
                    'vol_ok': vol_ok, 'posture': posture,
                    'vol_gate_threshold': float(self.vol_p60_threshold),
                    'scans_processed': self.scans_processed,
                    'trades_executed': self.trades_executed,
                    'bar_idx': self.bar_idx,
                    'timestamp': datetime.now(timezone.utc).isoformat(),
                    # Leverage envelope — for TUI slider
                    'leverage_soft_cap': getattr(self.eng, 'base_max_leverage', 8.0),
                    'leverage_abs_cap':  getattr(self.eng, 'abs_max_leverage',  9.0),
                    'open_notional':     round(open_notional, 2),
                    'current_leverage':  round(cur_leverage,  4),
                    'trade_direction_base': int(self.trade_direction),
                    'trade_direction_runtime': int(self._runtime_direction),
                    'efsm': self._efsm.snapshot() if self._efsm is not None else None,
                    'advanced_sl': self._advanced_sl.snapshot_dict() if self._advanced_sl is not None else None,
                }
            future = self.state_map.put('engine_snapshot', json.dumps(snapshot))
            future.add_done_callback(lambda f: None)
            # Heartbeat — MHS checks age < 30s; force blocking put to avoid
            # silent async drop/stall under client backpressure.
            if self.heartbeat_map is not None:
                hb = json.dumps({
                    'ts': time.time(),
                    'iso': datetime.now(timezone.utc).isoformat(),
                    'run_date': self.current_day,
                    'phase': 'trading',
                    'flow': 'nautilus_event_trader',
                })
                try:
                    self.heartbeat_map.blocking().put('nautilus_flow_heartbeat', hb)
                except Exception as hb_err:
                    log(f"  Heartbeat put failed: {hb_err}")
            # Persist capital so next restart resumes from here
            if capital is not None and math.isfinite(capital) and capital >= 1.0:
                self._save_capital()
        except Exception as e:
            log(f"  Failed to push state: {e}")
            
    def run(self):
        global running
        log("=" * 70)
        log("🐬 DOLPHIN Nautilus Event-Driven Trader Starting")
        log("=" * 70)
        
        self._build_engine()
        self._connect_hz()
        threading.Thread(target=self._heartbeat_loop, daemon=True).start()
        self._restore_capital()
        if self._restore_failed:
            log(f"RESTORE HALT: {self._restore_failure_reason}")
            self.shutdown()
            return
        self._rollover_day()
        self._restore_position_state()
        if self._restore_failed:
            log(f"RESTORE HALT: {self._restore_failure_reason}")
            self.shutdown()
            return
        # Seed the live snapshot immediately so engine_snapshot and
        # capital_checkpoint reflect the restored capital before scan traffic.
        try:
            posture = self._read_posture()
            self._push_state(self.bar_idx, 0.0, True, posture)
        except Exception as e:
            log(f"  Startup seed push failed: {e}")
        
        def listener(event):
            self.on_scan(event)
        
        self.features_map.add_entry_listener(
            key='latest_eigen_scan', include_value=True,
            updated_func=listener, added_func=listener
        )
        
        def exf_listener(event):
            self.on_exf_update(event)
            
        self.features_map.add_entry_listener(
            key='exf_latest', include_value=True,
            updated_func=exf_listener, added_func=exf_listener
        )
        
        log("✅ Hz listener registered")
        log(f"🏷️  ALGO_VERSION: {ALGO_VERSION}")
        log("⏳ Waiting for scans...")
        
        try:
            while running:
                time.sleep(1)
        except KeyboardInterrupt:
            log("Interrupted")
        finally:
            self.shutdown()
            
    def shutdown(self):
        log("Shutting down...")
        self._scan_executor.shutdown(wait=False)
        if self.eng and self.current_day:
            try:
                with self.eng_lock:
                    summary = self.eng.end_day()
                log(f"end_day: {summary}")
            except Exception as e:
                log(f"end_day failed: {e}")
        if self._market_state_runtime is not None:
            try:
                self._market_state_runtime.save()
            except Exception:
                pass
        if self.hz_client:
            try:
                self.hz_client.shutdown()
                log("Hz disconnected")
            except:
                pass
        log(f"🛑 Stopped. Scans: {self.scans_processed}, Trades: {self.trades_executed}")

def signal_handler(signum, frame):
    global running
    log(f"Signal {signum} received")
    running = False

def main():
    signal.signal(signal.SIGTERM, signal_handler)
    signal.signal(signal.SIGINT, signal_handler)
    trader = DolphinLiveTrader()
    trader.run()

if __name__ == '__main__':
    main()